Zambia, Egypt, Malawi and Mauritania join World Bank's $19bn minerals financing drive

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By bne IntelliNews October 18, 2026

Zambia, Egypt, Malawi and Mauritania are among the first eight countries selected for a World Bank Group initiative targeting $19bn in metals and minerals financing and private capital mobilisation through 2030, as African producers seek greater investment in mining, processing and supporting infrastructure.

World Bank President Ajay Banga announced the target on October 16 at the International Monetary Fund (IMF) and World Bank annual meetings in Bangkok, saying the institution had identified 20 potential partner countries, Reuters reported.

The World Bank aims to increase support more than fivefold, from $3.6bn during fiscal 2021–2025 to $19bn in fiscal 2026–2030. The target combines World Bank Group financing with private capital mobilisation rather than representing new lending commitments, and no specific share has been allocated to the African participants.

“In critical minerals, we plan to quintuple our business in five years,” Banga said.

The initiative forms part of the bank's Metals and Minerals strategy, which seeks to help resource-rich developing countries develop refining and manufacturing industries alongside mineral extraction.

Africa's mineral processing gap

Africa produces around 70% of the world's cobalt, three-quarters of its manganese and nearly one-fifth of its copper, but captures less than 1% of the value generated by manufacturing clean-energy technologies and components, according to International Energy Agency estimates cited by IntelliNews in September.

Reliable electricity, transport infrastructure, financing and technical skills remain major obstacles to expanding the continent's processing capacity.

See IntelliNews: Africa’s push for local mineral processing reshapes mining investment

The four African countries joined Argentina, Bolivia, India and Kazakhstan in launching the first partnerships and country compacts on October 16. These identify priorities for regulatory reform, infrastructure and investment rather than constituting approved financing packages.

The programme follows a separate United Nations initiative launched in September to help Guinea, Madagascar, Nigeria, Zambia and Zimbabwe develop higher-value mineral industries. Unlike the World Bank's financing and mobilisation target, the UN programme focuses on policy and regulatory assistance and has no announced funding allocation, IntelliNews reported on October 6.

See IntelliNews: Who stands to gain most from the UN’s critical minerals push in Africa?

Zambia targets copper expansion as Egypt develops mineral hubs

Zambia's World Bank compact focuses on increasing copper production and domestic processing through improved geological data, licensing, electricity supply and transport infrastructure. The country aims to produce 3mn tonnes of copper annually by 2031, alongside greater investment in supporting industries.

The expansion faces existing infrastructure constraints. Zambia temporarily suspended its 10% duty on copper-concentrate exports in 2025 and extended the waiver until September 2026 to help miners clear stockpiles during smelter maintenance, IntelliNews reported in June.

See IntelliNews: Zambia extends copper concentrate export-duty waiver, prioritising output over beneficiation amid smelter constraints

Egypt aims to increase mining's contribution to GDP to 6% by 2030 and raise metals and minerals-related exports to $9.8bn by 2040. Its compact includes regulatory reforms, improved geological information and mineral-processing hubs, supported by the World Bank Group and European Bank for Reconstruction and Development.

Malawi's programme prioritises mining governance, transport and energy infrastructure, local suppliers and greater transparency in mineral licensing and revenues.

Mauritania is seeking to combine mineral development with renewable energy investment to support lower-emission processing and increase the economic benefits retained domestically.

The four countries have different mineral resources and industrial capacities, but their compacts share an emphasis on infrastructure and investment conditions. Neither the national production targets nor the identified projects constitute guaranteed outcomes of the World Bank initiative.

Financing, guarantees and competition for African minerals

The World Bank will provide policy advice and public financing, while its private-sector arm, the International Finance Corporation (IFC), will invest and mobilise commercial capital. The Multilateral Investment Guarantee Agency (MIGA) will offer guarantees aimed at reducing investment risks.

For African mineral producers, the availability of electricity, transport links and commercially viable processing facilities will be critical to attracting investment beyond extraction.

The programme comes amid growing competition for copper, cobalt, manganese and other minerals used in electric vehicles, batteries, renewable energy equipment and advanced manufacturing.

The United States, the World Bank's largest shareholder, has pressed the institution to expand support for mineral extraction, processing and infrastructure as Washington seeks to diversify strategic supply chains.

The US International Development Finance Corporation (DFC) said on October 9 that it planned to take more equity stakes in African critical-minerals projects. It has committed more than $3bn to critical minerals and related investments in Africa.

See IntelliNews: US development agency plans more direct investment in African critical minerals

US Treasury Secretary Scott Bessent also called for World Bank involvement in deep-sea mining during the annual meetings. Banga did not endorse such projects, while the bank's strategy calls for a precautionary approach consistent with its environmental and social policies.

Separately, six multilateral development banks, including the World Bank and African Development Bank, have agreed to coordinate financing, guarantees, infrastructure investment and environmental standards for critical-mineral supply chains.

The collaboration could provide additional channels for African countries seeking investment in electricity networks, transport corridors and processing facilities, although neither initiative establishes a country-specific financing allocation.

GOOD NEWS

Greenland to introduce ban on seabed mining  

Staff Writer | October 16, 2026 | 

Near Kulusuk, eastern Greenland. Stock image.

Greenland Parliament has unanimously passed a resolution to introduce a ban on seabed mineral extraction, RMF24 reported Friday.  

The news comes a day after US President Donald Trump announced “massive oil, gas and mineral exploration” was set to begin on Greenland.  

Trump described the Danish Arctic island, which is run by a semi-autonomous government, as “our new and beautiful Greenland” in a social media post that hailed what he said were US gains from a security deal signed in September. 

The resolution stipulates the memorandum on deep-sea mining is to remain in force until the end of 2029, RMF24 reported, with the possibility of extension for further two-year periods. The legislation is expected to be passed in the first half of 2027.  

Greenland has been caught up in a confrontation with Trump who has been saying since 2019 the US should buy or take the strategically located and mineral-rich island for security reasons. On several occasions, he refused to rule out force. 

Greenland has one of the world’s most significant underexplored critical mineral endowments, positioning the territory at the center of critical minerals and defense supply chain strategies. 

The Tanbreez property in Greenland, with a 28.2 million tonne TREO resource, has been ranked as one of the world’s top rare earth projects. Rare earth explorers are drawn to Greenland to dig into what the US Geological Survey calls the world’s biggest undeveloped deposits of rare earth metals. 

While the US signalled in September it is nearing approval for permits to mine the deep sea, the specter of seabed mining being allowed to commence has alarmed many environmentalists due to concern it could cause irreversible biodiversity loss. 

Parts of the Pacific Ocean and elsewhere are estimated to contain large amounts of potato-shaped rocks known as polymetallic nodules that contain the building blocks for electric vehicles, weapons and electronics. 

Scientists at the United States Geological Survey (USGS) have discovered that some metal-rich rocks recovered from the deep ocean can spontaneously combust when exposed to air and processed, raising unexpected safety concerns for a potential new mining industry. 

BAN DEEP SEA MINING

Deep-sea mining faces surprise risk of rocks catching fire

Cecilia Jamasmie | October 14, 2026 | 

Environmental groups warn seabed mining may harm fragile marine habitats and disrupt ecosystems. (Stock image by magdal3na.)

Scientists at the United States Geological Survey (USGS) have discovered that some metal-rich rocks recovered from the deep ocean can spontaneously combust when exposed to air and processed, raising unexpected safety concerns for a potential new mining industry.

The finding follows a 2022 research expedition to the Escanaba Trough, off the Oregon–California border, where two of 57 hydrothermal rock samples ignited during laboratory processing. 

The reactions generated temperatures exceeding 100°C and completely oxidized the original material, according to a study published in Nature’s Scientific Reports.

“The production of heat through exothermic reactions has not been investigated in seafloor massive sulfide deposits but has the potential to cause rapid onset fires on ships,” the researchers wrote.

The discovery introduces another challenge for companies seeking to extract valuable metals from the ocean floor. While deep-sea sulfide deposits contain copper, zinc, gold and silver, their behaviour during recovery, transportation and processing remains poorly understood, the researchers noted. 

The findings could influence safety standards as governments and mining companies assess the commercial potential of seabed resources.

Unexpected reactions

The USGS collected the samples in collaboration with the Bureau of Ocean Energy Management (BOEM) and the National Oceanic and Atmospheric Administration (NOAA), using a remotely operated vehicle to investigate four regions of the Escanaba Trough.

Located within the US Exclusive Economic Zone, the trough contains mineral deposits formed by hydrothermal activity that concentrates metals, including copper, zinc and iron, on the seafloor.

Most of the recovered rocks contained substantial quantities of metal sulfides. However, when researchers freeze-dried and crushed the samples in the laboratory, two underwent rapid oxidation and generated enough heat to ignite.

To determine what caused the reactions, scientists compared the samples’ mineral composition, chemistry and thermal behaviour.

They initially considered whether petroleum or tar trapped within the deposits could explain the combustion. Hydrothermal processes and heavy sedimentation can produce petroleum-like materials capable of self-heating when exposed to oxygen.

Instead, the investigation identified nanocrystalline marcasite, an unstable form of iron sulfide, as the likely culprit.

The scientists determined that exposure to oxygen and mechanical forces from crushing or grinding could trigger heat-generating oxidation in the rocks.

“The new research suggests that some seafloor massive sulfide rocks have physical and chemical properties that could make them behave very differently from comparable mineral deposits mined on land,” the USGS said in a statement.

The findings do not establish that all marcasite-bearing deposits present a combustion hazard. Rather, they identify specific mineral characteristics that may warrant closer examination before commercial extraction begins.

Mining implications

The research comes as interest in deep-sea mineral resources grows, driven by demand for metals used in infrastructure, manufacturing and energy technologies.

The International Seabed Authority (ISA) has awarded exploration contracts for polymetallic sulfide deposits along the Northern Mid-Atlantic Ridge and Southwest Indian Ridge, although commercial extraction under those contracts has not begun.

Unlike minerals that remain under relatively stable conditions on the seafloor, recovered sulfide rocks would encounter major environmental changes during mining operations.

“If seafloor massive sulfide mining occurs, rocks brought to the surface would experience conditions dramatically different from those on the seafloor, including exposure to oxygen and changes in temperature, pressure, and moisture,” the USGS said.

“Such changes could affect how sulfide minerals react and how much heat they generate.”

Those risks could require operators to assess mineral stability before extraction and develop procedures for handling, storing and transporting potentially reactive material. The possibility of heat buildup aboard vessels presents a particular concern where large quantities of crushed sulfide rock could be concentrated in enclosed spaces.

Deep-sea mining companies The Metals Company (Nasdaq: TMC) and Deep Sea Minerals (CSE: SEAS) did not respond to MINING.COM’s requests for comment on potential operational safety concerns or whether they had assessed similar risks.

The findings add an occupational safety dimension to an industry already facing scrutiny over its potential environmental effects.

Environmental groups have warned that seabed mining could damage fragile marine habitats, disturb sediments and threaten ecosystems that scientists have only begun to understand. The consequences of large-scale extraction remain uncertain, particularly in deep-ocean environments where recovery from disturbance may be slow.

Chinese researchers found in early October 11,700 new viruses in the Clarion-Clipperton zone, where the ISA has issued 31 exploration contracts. Bowen Hou and Lilan Zhang of Chongqing University, and Dong Sung of China’s Second Institute of Oceanography, investigated the ‘virome’ of the Clarion-Clipperton to establish what they call a “vital pre-mining baseline of microbial ecosystem for future environmental impact assessments.” They’ve published their results in Nature Communications.

For prospective operators, the USGS research suggests that conventional approaches to handling sulfide ores may not adequately address the conditions encountered during deep-sea mining.

Further testing will be needed to determine how widely reactive marcasite occurs in seabed deposits and whether the laboratory results translate into comparable hazards during industrial operations.

BAN DEEP SEA MINING

Study Suggests Deep Sea Mining Could Be a Loss-Making Proposition

Image courtesy of The Metals Company

Published Oct 6, 2026  by The Maritime Executive

Any deep-ocean seabed operation is an expensive proposition, as research vessel operators and offshore drilling companies are well aware. Deep-sea mining for manganese nodules would be no exception, demanding specialized tonnage, robotics, processing equipment and R&D. In a new report, some of the budding industry's opponents suggest that the overhead of industrial-scale subsea operations could make deep sea mining a loss-making proposition - even before accounting for environmental factors. 

Drawing on an investment prospectus for two projects belonging to The Metals Company, one of the biggest deep sea mining firms, the environmental consultancy Koinon conducted "stress-tests" to see if TMC's business model would hold up under various scenarios. The study was sponsored by five NGOs and foundations that are opposed to rapid commercialization of the practice. Koinon's analysts looked at typical cost overruns for megaprojects and ran 10,000 simulations on various outcomes, finding that the median outcome would result in no meaningful return. 

"In 60% of iterations, the collector’s net cash flow is negative in every year, with the consequence that no internal rate of return exists," Koinon's analysts wrote. "Among the minority of runs that do generate a positive return profile, the median IRR stands at 4%." 

The report concludes that the project has an 83 percent probability of returning a loss, with a median net present value of -$5 billion, assuming a discount rate of 8 percent. By comparison, under TMC's own projected scenario, the two projects would make $23 billion. 

The consultancy's analysis found that the up-front capex cost of the collection system was the primary driver of profitability, and the only variable capable of moving the project's margins into the zone of positive returns. Koinon estimated that - based on typical cost overruns for a frontier industry - the average simulated cost of the collection system would likely be about 1.9 times TMC's expected cost, based on typical overruns in past reference cases in offshore oil and gas, frontier technology and mega-scale mining projects. In its sensitivity analysis, changes in capex alone could vary the size of the return by more than $8 billion, making capex alone determinative of project profit or loss. 

The discount rate is a key element in this analysis. Koinon noted that the regulatory and environmental risk associated with deep sea mining has prompted dozens of bankers, insurers and industrial-metals buyers to publicly vow not to do business with deep-sea minerals producers - potentially making project financing more expensive (and thereby driving up the discount rate).  

"The project’s own economics, when simulated rather than asserted, show no return in the majority of cases and a loss in the large majority, while the financing environment strips the cheapest layers from the capital stack, with the consequence that any viable project is forced onto equity priced for speculative risk," Koinon concluded. "The required return that follows, in the order of 25 to 30%, sits above the entire institutional private-markets universe against which it might be benchmarked."

Separately, the consultancy analyzed potential tax revenues from the project and determined that the net benefits for national governments - including project sponsor states - would be slim. The International Seabed Authority would reap substantial revenues from project royalties, but these would be offset by declining tax revenue from onshore mining projects. (The tax revenue drop would be a consequence of higher supplies and lower prices for nickel, cobalt, copper and manganese.)

"This study confirms that deep-sea mining remains economically unattractive from a commercial investment perspective," asserted Torsten Thiele, founder of study co-sponsor Global Ocean Trust. "The findings also highlight that the risks extend beyond mining companies to sponsoring states and countries dependent on terrestrial mining."

TMC asserts that the environmental risks of deep-sea mining can be managed, and that it can earn an after-tax internal rate of return (IRR) of 27 percent on its first projects. The firm argues that the world's economies need a new source of industrial metals, and that manganese nodule extraction can provide that supply with a lower environmental impact than that of shoreside mining projects. 

Opponents argue that the long term environmental effects of deep-sea mining are unknown, and that little-studied seabed ecosystems will be disturbed by the removal of manganese nodules from the bottom. Risks include sediment releases into the water column, long-term habitat alteration, and increased odds of extinction for certain vulnerable species, according to some researchers and activists.

 

NZ Activists Board Research Vessel in Protests Against Deep-Sea Mining

Greenpeace activists boarded the vessel when it ruetnred to New Zealand after a deep-sea survey (Greenpeace)

Published Sep 28, 2026 7:16 PM by The Maritime Executive

Greenpeace activists in New Zealand are intensifying their opposition to deep-sea mining. They boarded a state-owned research vessel that was returning from an exploration mission in the Pacific on behalf of a U.S. company.

On September 26, the activists boarded the RV Tangaroa, New Zealand’s only ice-strengthened and dynamically positioned deep-water research vessel, to protest the country’s role in facilitating U.S. company Glomar Minerals’ plans to start deep-sea mining in the Pacific Ocean.

The 1991-built Tangaroa, which is 70 meters (230 feet) in length, had just returned to Wellington Harbor after completing exploration work for Glomar. An activist dressed like U.S. President Donald Trump flew an American flag from the vessel while another held a banner reading “Don’t Mine the Moana.” The activists occupied the vessel for about seven hours before they were removed by the police.

Operated by Earth Sciences NZ (formerly NIWA), Tangaroa is equipped for ocean science, exploration, and marine engineering and has carried out environmental survey and ocean science research missions across the South Pacific, Southern Ocean, and Antarctic waters. The vessel is equipped with a range of modern and sophisticated equipment that enables the sampling and measurement of ocean environments.

Greenpeace highlighted that its activists boarded the vessel following her return from the Clarion- Clipperton Zone (CCZ) in the international waters of the Pacific, where the ship surveyed deep-sea minerals. The research was carried out on behalf of UK Seabed Resources Ltd (UKSR), a wholly-owned subsidiary of Glomar Minerals.

According to Glomar Minerals, Tangaroa had departed Tahiti in late July for the CCZ to survey its license area for polymetallic nodules rich in critical minerals. The campaign was the first comprehensive geological and biological survey of the UK2 license area in more than 40 years. UKSR’s license areas cover approximately 133,000 square kilometers of deep seafloor, an area covered in polymetallic nodules containing manganese, cobalt, nickel, copper, and rare earths in a single ore.  

The Tangaroa returned to Wellington Harbor after a 65-day campaign armed with environmental and resource data that is expected to inform Glomar Minerals’ decision to embark on commercial exploitation as soon as next year.

“If deep-sea mining is allowed to go ahead, it will strip the seafloor, risking irreversible damage to the ocean we all rely on, to supply minerals for Trump’s war machine. Pacific Peoples are at the frontline of this fight, and deep sea mining puts their way of life and cultural heritage at risk. It must not be allowed to start,” said Juressa Lee, Greenpeace Aotearoa campaigner.

Deep sea mining has become a highly controversial and divisive issue in New Zealand, with environmentalists and activists asserting that it causes massive damage to ocean ecosystems as well as having adverse economic impacts for coastal communities.

President Trump has emerged as the biggest proponent of deep-sea mining. In April last year, he issued an Executive Order to fast-track commercial deep-sea mining in the U.S. and international waters. Earlier this year, the U.S. government launched Project Vault, an initiative to secure a global supply chain and stockpile a reserve of minerals for military and industrial purposes.

Greenpeace contends that the Tangaroa expedition comes as the U.S. is preparing to issue unilateral commercial permits for seafloor minerals, a move that bypasses the International Seabed Authority (ISA), where UN member states are negotiating the regulations that will guide deep-sea mining.

The Tangaroa is no stranger to protest. In 2015, Greenpeace activists occupied the vessel in protest of its use by the international oil industry to survey the waters of Aotearoa for deep-sea oil drilling.

New Zealand Invests $20 Million in New Offshore Gas Projects

NOAA file image

Published Sep 27, 2026 10:05 AM by The Maritime Executive

New Zealand has made further progress in restarting domestic gas production with a new round of funding targeting offshore exploration projects. Resources Minister Shane Jones and Associate Finance Minister Chris Bishop this week announced $21 million financing for two offshore gas projects. The financing will be made through the Gas Security Fund (GSF), a $113 million state-owned investment facility to ramp up gas production in New Zealand.

One of the projects considered for funding in this round is EnZed Energy’s Kaheru offshore gas exploration in Taranaki Basin. The project will receive $12.19 million for a time-limited equity arrangement. The project has a total cost of $40.2 million with EnZed Energy seeking to raise the remaining $28.1 million from other investors. Initially, the project will prospect the Kaheru field to determine whether commercially recoverable gas is present.

The Australian explorer EnZed Energy was awarded gas exploration permit by New Zealand in July. It was the first such rights to be issued by the country after reversing the 2018 ban on offshore oil and gas exploration. The 12-year exploration permit covers about 546 square kilometers in the offshore Taranaki Basin, east of the producing Kupe field.

“This project targets an estimated 182 petajoules(pj) of additional gas reserve in the Kaheru permit. That’s equivalent of nearly 25 percent of New Zealand’s January 2026 proven and probable gas reserves,” said Minister Shane Jones.

According to government petroleum data, New Zealand’s natural gas proven plus probable reserves as of 1 January 2026 was 731 pj, a decline of 23 percent from last year. The Ministry of Business noted that this was the lowest level of natural gas reserves since recording began 20 years ago. Jones added that projects such as Kaheru could be a significant new source of domestic gas supply, helping meet New Zealand’s energy needs in the coming years.

The other project to receive the funding is led by the oil services company Schlumberger New Zealand (SLB). The company was awarded $8.2 million to provide industry players with free access for two years to its proprietary seismic data, spanning New Zealand’s key exploration provinces. Over the years, SLB has assembled an extensive seismic library, comprising over 28,000 km2 of 3D data and 5,000 km2 of 2D seismic coverage across both the Taranaki and East Coast basins. As part of the funding, the government wants SLB to combine this fragmented subsurface exploration information into a single, basin-scale, AI-enabled dataset, to be delivered in industry standard format.

 

 

Require satellite evidence for energy and AI data center approvals, researchers urge governments

University of Surrey

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Satellite data could help governments decide where to build the data centres behind the growth of AI, by showing where renewable power, water and land are available and where drought and climate risks are highest, according to researchers at the University of Surrey. 

Writing in Nature Reviews Clean Technology, the team argues that Earth observation (information about the planet's systems gathered largely by satellites), which is often underused, should become a formal part of how governments, regulators and investors plan, run and check the clean energy transition. 

The call from Surrey researchers comes as demand floods the queue to connect to Britain's electricity grid. According to Ofgem, contracted demand in that queue rose from 41 gigawatts to 125 gigawatts between November 2024 and June 2025, driven largely by data centre projects. 

Globally, the International Energy Agency expects electricity use by data centres to double from 485 terawatt-hours in 2025 to 950 terawatt-hours by 2030, roughly the amount of electricity Japan uses in a year. 

The authors say geospatial and satellite data can support more strategic data centre planning by combining information on renewable energy potential, land-surface temperature, open water and groundwater availability, drought exposure, biodiversity, land-use constraints and climate-risk projections.  

Dr Ana Andries, Senior Lecturer in GIS, Remote Sensing and Environmental Assessment and lead author of the Comment from the University of Surrey, said: 

  

"Every new data centre is a long-term decision about power, water and land, while also an increasing source of global carbon emissions. Satellites already give us detailed and regularly updated information on all three, yet that evidence is too often left out of the decisions that matter. We're asking governments to write it into the rules, so the infrastructure behind the AI boom goes where the grid, renewable energy potential and the landscape can support it." 

The Comment also sets out how satellites and related geospatial data support planning, operation and monitoring. For planning, the Global Solar Atlas uses satellite data to map solar power potential worldwide, while MapYourGrid, an open-source community project, is mapping power lines, substations and power plants so planners can see where new generation can connect. Once infrastructure is running, thermal imaging from space could help identify faults such as hot spots on solar panels, while radar from Sentinel-1 satellites can map wind at the sea surface around offshore wind farms, including the slower air in the wakes the farms leave behind. 

Water is one area where energy systems are exposed. For instance, this summer's drought pushed the Danube to record lows and forced Romania to shut down both reactors at the Cernavodă nuclear plant, which normally supplies about a fifth of the country's electricity. Climate data and satellite monitoring of river levels, reservoirs and drought can help estimate risks to hydropower and to power stations that rely on river water for cooling, and help protect a dispatchable electricity supply. For accountability, some methane-sensing satellites detected releases as small as 0.03 tonnes per hour in single-blind controlled tests, though performance varied between systems. This gives regulators a way to check emissions from oil and gas infrastructure. Satellite imagery has also been used to map mining expansion linked to clean-energy supply chains in the Democratic Republic of Congo. 

The authors call on governments and regulators to require satellite evidence in renewable energy zoning, infrastructure permitting, climate-risk screening and environmental impact assessment, especially where projects affect land, water, biodiversity or grid access. Energy plans and regulatory reporting should also set out clear data standards, validation requirements and how uncertainty is reported. For investors and utilities, they argue, satellite data should become part of due diligence and asset-risk assessment. Satellite data should support these decisions, they add, not replace ground data, local knowledge or official reporting. 

Delivering this will need capacity building, the authors say – energy ministries, regulators, utilities, local authorities and investors need trusted satellite products, practical guidance, standards, training and evidence that the approach is cost-effective. 

Professor Ravi Silva, Distinguished Professor and Director of the Advanced Technology Institute at the University of Surrey and co-author of the Comment, said: 

"Building a clean energy future requires thousands of decisions, from choosing locations for solar farms, wind turbines, transmission infrastructure and energy storage to ensuring these assets operate effectively over their lifetime. Better evidence helps make these decisions more efficient, cost-effective and reliable. Satellite data can help identify areas with strong solar and wind resources, map existing infrastructure, detect operational issues, and provide independent information to support monitoring and oversight." 

### 

Notes to editors 

• Citation: Andries, A., Xuan, J., Liu, L. et al. Leverage Earth observation data to support the clean energy transition. Nat. Rev. Clean Technol. (2026). https://doi.org/10.1038/s44359-026-00220-y 

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Journal

Nature Reviews Clean Technology

DOI

10.1038/s44359-026-00220-y

Article Title

Leverage Earth observation data to support the clean energy transition

Learning maths goes (slightly) better with AI, but teacher plays a key role

Primary school pupils who use an AI-supported learning tool learn arithmetic more quickly than children who do not use such a tool

Radboud University Nijmegen

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Primary school pupils who use an AI-supported learning tool learn arithmetic more quickly than children who do not use such a tool. This is the finding of a multi-year study by Radboud University involving nearly eight thousand children. However, the researchers warn that it's no miracle cure, as the teacher’s role remains essential even with AI.

The researchers examined the development of numeracy skills among 7,885 Dutch primary school pupils over a three-year period, when these systems were first used between 2014 and 2017. They compared classes that used an adaptive learning system with classes that taught numeracy without such a system. Such a system automatically adapts exercises to the level of an individual pupil. Pupils are given more difficult tasks if they perform well and simpler tasks if they get stuck. On average, pupils who used the system showed more positive growth in their maths performance over the years.

Large-scale study

‘It is one of the first studies to examine the long-term effects of adaptive learning tools. Until now, research into these kinds of systems has mainly focused on short-term effects, or has been conducted on a smaller scale,’ explains Susanne de Mooij, an education researcher and one of the study’s authors.

‘Adaptive learning systems have been in use for almost fifteen years in more than half of all Dutch primary schools. Nevertheless, some fear that this type of adaptive learning tool leads to pupils learning less effectively, as digital learning materials are thought to be less effective than paper-based ones. This study shows that, in cognitive terms at least, this is not the case.’

Greater success at large schools

The effects found are modest, De Mooij emphasises. ‘We have found small effects, but generally a positive trend. Moreover, the positive effect is particularly evident in large schools and in vulnerable schools with many pupils from less advantaged socio-economic backgrounds. There is often greater variation between pupils in such settings. Adaptive technology can then help to better tailor teaching to individual needs,’ says De Mooij.

This means that an adaptive learning tool is primarily something that supports teachers, rather than replacing them. ‘A teacher cannot adapt the lesson content to thirty pupils at the same time. But with a tool like this, you can improve pupils’ maths skills whilst also reducing a teacher’s workload.’ The researchers emphasise that the teacher always remains in control: ‘The teacher draws up the lesson plan, decides what pupils learn and oversees what happens in the classroom. Such learning technology is fed solely by the data stored in the system, whereas a teacher has access to much more information.’

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Journal

Learning and Instruction

DOI

10.1016/j.learninstruc.2026.102480

Article Title

Arithmetic development: a longitudinal observational study into the use of adaptive technology

Article Publication Date

1-Oct-2026

AI scientist autonomously generates and validates new biological discoveries

Chalmers University of Technology

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image: 

Researchers have created a closed-loop AI laboratory, capable of conducting research on brewer’s yeast. It could identify biological questions, recommend experiments and evaluate experimental outcomes. The image shows the robot scientist Eve, which was specifically designed for drug discovery, and which has now been updated with large language models and automated reasoning.

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Credit: Credit: NIH Image Gallery/Chalmers University of Technology

Researchers at Chalmers University of Technology in Sweden have developed an AI scientist capable of generating scientific hypotheses, designing experiments, and interpreting results. Making new biological discoveries with minimal human intervention is an important advance in self-driving laboratories.

By combining advances in large language models, automated reasoning and laboratory automation, the researchers created a closed-loop AI laboratory, capable of conducting research on brewer’s yeast, Saccharomyces Cerevisiae. As the basis of the research, the AI was input scientific knowledge, including the yeast’s genome, metabolism and previous studies.

“It is too much information for a human to analyse, but our AI scientist could identify promising biological questions, recommend experiments to test them, evaluate experimental outcomes and iteratively refine its understanding based on new evidence. Rather than serving solely as decision supporting tools, the AI scientist actively generates new scientific knowledge”, says Ievgeniia Tiukova, postdoctoral researcher at the department of Life Sciences at Chalmers University of Technology, and one of the authors of the new study.

Integrating the thinking power of AI with an experimental capability is unusual, and cutting edge within a rapidly expanding area, where AI scientists autonomously perform extensive research. Tiukova compares the development to that of self-driving cars, where AI and machine learning are also used to process information, draw conclusions, and take action.

According to Ross King, Professor at the Department of Computer Science and Engineering at Chalmers and the University of Gothenburg, and the study’s senior author, autonomous laboratories will revolutionise research by systematically investigating biological systems much faster than is possible today.

“AI Scientists will collaborate with human scientists to accelerate discoveries across biology, medicine and biotechnology. Such AI systems have the potential to reduce the time required to explore complex scientific questions, and optimise the use of laboratory resources”, says Professor King.

Both authors emphasise that autonomous AI will, for now, augment rather than replace scientists, by increasingly undertaking the routine cycles of hypothesis generation and experimental testing.

“Human scientists remain essential for defining research priorities, interpreting broader scientific significance and ensuring ethical oversight. Future generations of autonomous discovery systems will become increasingly capable of collaborating with human scientists, becoming valuable partners in addressing some of the most challenging questions in biology and medicine”, says Professor King.

Captions: 
Top image: Researchers have created a closed-loop AI laboratory, capable of conducting research on brewer’s yeast. It could identify biological questions, recommend experiments and evaluate experimental outcomes. The images shows the robot scientist Eve, which was specifically designed for drug discovery, and which has now been updated with large language models and automated reasoning. Credit: NIH Image Gallery/Chalmers University of Technology

Second image: Ievgeniia Tiukova and the Robot scientist Eve

About AI scientists

Professor Ross King was the first to develop the concept of a general-purpose robot scientist. His first robot scientist, Adam, was designed to autonomously carry out scientific experiments and generate new knowledge. He later developed a second robot scientist, Eve, which was specifically designed for drug discovery.

The underlying concept of using robotic systems to automate and accelerate scientific discovery has since been extended to other areas of research, including chemistry and other specialised scientific tasks.

 

More about the research:

The study Agentic AI integrated with scientific knowledge: laboratory validation in systems biology was published in the Journal of the Royal Society Interface.

The authors of the study are Daniel Brunnsåker, Alexander H. Gower, Prajakta Naval, Erik Y. Bjurström, Filip Kronström, Ievgeniia A. Tiukova and Ross D. King. The researchers are affiliated with Chalmers University of Technology, the University of Gothenburg and the University of Cambridge in the UK.

The study has received funding from the Wallenberg AI, Autonomous Systems and Software Program (WASP), the UK Engineering and Physical Sciences Research Council, Chalmers AI Research Centre (CHAIR), and the Swedish Research Council for Sustainable Development, Formas.

The robot scientist Eve, which was specifically designed for drug discovery, has now been updated with large language models and automated reasoning.

Credit

Chalmers University of Technology

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Journal

Journal of The Royal Society Interface

DOI

10.1101/2025.06.24.661378

Method of Research

Computational simulation/modeling

Subject of Research

Not applicable

Article Title

Self-driven biological discovery through automated hypothesis generation and experimental validation

AI-driven analytics and data-mining strategies for drug discovery

New volume of SLAS Discovery highlights innovative screening platforms, AI-driven analytics and data-mining strategies for treating oncology, infectious disease, immunology and computational biology

SLAS (Society for Laboratory Automation and Screening)

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SLAS Discovery, Vol. 42

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Credit: SLAS

Oak Brook, IL – Volume 42 of SLAS Discovery includes one review, eight original research articles and 1 short communication for oncology, infectious disease, immunology and computational biology research through innovative screening platforms for accelerating the discovery of targeted therapies, diagnostic biomarkers, and mechanistic insights across diverse disease areas.

Review

• ADCs for Colorectal Carcinoma: Decoding Clinical Evidence for Molecular Design Innovation
  This review examines the rapidly evolving landscape of antibody-drug conjugates (ADCs) in colorectal cancer (CRC), highlighting the recent approval of T-DXd for HER2-positive disease as a milestone entry into this treatment arena. The authors explore clinical performance, design considerations and future directions, positioning ADCs as a promising targeted option for CRC patients with unmet clinical needs.

Original Research

• Identification of AMPD2 Allosteric Inhibitors with Novel Mechanism of Action by Fragment Merging Approach
  Using an X-ray fragment screening approach, researchers identified a series of allosteric inhibitors that selectively target Adenosine monophosphate deaminase 2 (AMPD2) over other AMPD isozymes, overcoming the poor selectivity of traditional orthosteric inhibitors. Through iterative fragment merging and optimization, the team developed potent compounds (10g and 10h) that bind a previously uncharacterized allosteric site, offering valuable tool compounds for studying AMPD2's roles in nucleotide metabolism, energy homeostasis, and immune oncology.
• Thermodynamic Profiling and Fragment Screening of GPCRS Using Grating-Coupled Interferometry
  Researchers demonstrate that grating-coupled interferometry (GCI) offers a powerful biosensor platform for characterizing GPCR–ligand interactions, providing high-quality kinetic data comparable to established Biacore technology while enabling rapid affinity and thermodynamic profiling from single-concentration injections. Using the adenosine A2A receptor as a model, the team validated the approach through kinetic fragment screening of a 704-member library, identifying specific binders confirmed by nanoDSF and establishing GCI as an information-rich tool for early-stage GPCR drug discovery.
• Identification of Novel Diagnostic Biomarkers and Host-Directed Drug Screening for Mycobacterium avium Infection: a Multi-Omics and Artificial Intelligence Study
  By integrating single-cell RNA sequencing with machine learning, researchers mapped the immune landscape of Mycobacterium avium (MAV) infection. They uncovered a monocyte-driven MIF-APP signaling axis that recruits and "locks" macrophages into a hyper-inflammatory state, explaining the paradox of high inflammation but poor pathogen clearance. The study also developed a five-gene diagnostic signature (AUC > 0.88) and identified Wogonin as a potential host-directed therapeutic that targets STAT3 and TNF to break the immune impasse.
• Development of a Rapid and Sensitive EnLIGHT OMEGA Assay for Extracellular AGR2 Detection In Biological Fluids
  Researchers developed a novel homogeneous OMEGA assay to detect extracellular AGR2 (eAGR2), a protein strongly linked to tumor progression and cancer aggressiveness, with a broad dynamic range (0.02–300 ng/mL) and no washing or separation steps required. This sensitive, rapid method outperforms conventional ELISA and is applicable across diverse biological fluids, offering a valuable tool for cancer biology, drug resistance and biomarker discovery studies.
• Use t Ttests to Analyze Counts of Cells in Two States
  A comparative analysis of statistical tests for count data found that t tests perform as well as or better than specialized count-based methods at maintaining false-positive rates, with no disadvantage in detecting real differences. The findings reassure researchers that converting count data to percentages and analyzing with t tests is a valid and effective approach under typical wet-lab conditions.
• In Silico Prioritization and Cheminformatics Identify Structurally Diverse Small-Molecule Inhibitors
  Using a computational data-mining strategy applied to a previously deposited screen of nearly 300,000 small molecules, researchers identified three distinct chemical scaffolds that inhibit Lassa virus cell entry with potencies as low as 10 nM and strong selectivity over related viruses. These compounds act at the membrane fusion stage by targeting pH-sensitive regions of the viral glycoprotein, highlighting the power of combined computational and experimental approaches for antiviral drug discovery.
• CellVision: A Deep Learning Based Image Analysis Platform to Accelerate Immuno-Plaque Assay Data Processing for Dengue Vaccine Development
  Researchers have developed CellVision, a deep learning-based workflow that fully automates viral plaque counting in high-throughput immuno-plaque assays, accurately segmenting fused plaques and distinguishing them from other objects without any manual image review. Integrated into Merck’s (& Co., Inc.) µPlaque assay to support an investigational dengue vaccine, CellVision outperformed a commercial alternative and sets a new standard for AI-powered analysis in antiviral vaccine discovery.
• Tumor-Versus-Nonmalignant Quantitative Drug Sensitivity Profiling Identifies capivasertib as a Selective Therapeutic Candidate for Nasopharyngeal Carcinoma
  Using a tumor-normal-paired high-throughput drug screening approach against EBV-positive and EBV-negative nasopharyngeal carcinoma (NPC) cell lines, researchers identified the AKT inhibitor capivasertib as a highly selective anti-NPC agent that spares normal epithelial cells. Capivasertib synergized with platinum-based chemotherapy, enhanced radiosensitivity, and significantly prolonged survival in combination with cisplatin in xenograft models, providing a strong rationale for its clinical evaluation in advanced NPC.

Short Communication

• Development of a High-Throughput TR-FRET Assay for Identification of Small Molecule Inhibitors of the LILRB4 (ILT3)-SCG2 Immune Checkpoint Interaction
  Researchers have developed a high-throughput TR-FRET assay to interrogate the interaction between the immune checkpoint LILRB4 (ILT3) and its ligand SCG2, a pathway driving myeloid-mediated immunosuppression in the tumor microenvironment. Pilot screening identified two compounds, BMS-813,160 and PSB-603, that dose-dependently inhibit this interaction with micromolar potency, providing the first small-molecule modulators of the LILRB4-SCG2 axis and a foundation for targeting myeloid-driven immunosuppression.

Access to this volume of SLAS Discovery is available at https://www.slas-discovery.org/issue/S2472-5552(26)X2003-2

All active SLAS Discovery and SLAS Technology call for papers are available at: https://www.slas.org/publications/call-for-papers/

*****

SLAS Discovery reports how scientists develop and use novel technologies and/or approaches to provide and characterize computational, chemical and biological tools to understand and treat human disease. The journal focuses on drug discovery sciences with a strong record of scientific rigor and impact, reporting on research that:

• Enables and improves target validation
• Shares and/or compare novel methods for medium- or high-throughput screening
• Evaluates current drug discovery technologies
• Provides novel research tools
• Incorporates research approaches that enhance depth of knowledge and drug discovery success

SLAS (Society for Laboratory Automation and Screening) is an international professional society of academic, industry and government life sciences researchers and the developers and providers of laboratory automation technology. The SLAS mission is to bring together researchers in academia, industry and government to advance life sciences discovery and technology via education, knowledge exchange and global community building.

SLAS Discovery: Advancing the Science of Drug Discovery, 2025 Impact Factor 3.3. Editor-in-Chief Robert M. Campbell, PhD, Grove Biopharma, Inc., Chicago, IL (USA)

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Journal

SLAS DISCOVERY

Motion Style Slider: novel framework enabling fine control of nuances in character motion

Directors and animators can now modulate the degree of emotion, energy, and expression without intermediate motion-capture data

Reports and Proceedings

Institute of Science Tokyo

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Using a neutral motion and a stylized motion as inputs, the system continuously controls the strength of the style by varying the slider value α.

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Credit: Chen-Chieh Liao

Novel framework enables fine control over the intensity and expressiveness of AI-generated human motion, as reported by researchers from Science Tokyo. The “Motion Style Slider” framework enables animators to control the style and adjust a virtual character’s motion, from subtle to overly exaggerated, based on only two motion samples. The proposed approach achieves consistent style control and smooth transitions without the need for extensive motion-capture datasets.

In the production of videogames and animated movies, directors and animators are constantly fine-tuning how a character’s movements should appear. In many cases, the directors require subtle changes, such as making a character appear a little more restrained or a bit more expressive, and subjectively communicate the degree or extent to which the change needs to be applied. Commercially available artificial intelligence (AI) tools can generate stylized animations for virtual characters, transforming a neutral motion into one that conveys particular styles like joy, anger, exhaustion, or confidence. However, a more refined modulation demands more than simply applying a style; it requires precisely controlling how much of that style comes through.

Most existing tools fall short in this regard, offering only fixed versions of a given emotion or expression with no way to increase or reduce the intensity. The main reason behind this is that AI-based motion-generation systems are often trained using fixed examples of the same action, namely a neutral motion and fully stylized versions obtained through motion-capture technology. Creating additional recordings at multiple intermediate levels of expression would require multiple long motion-capture sessions, making it costly and impractical for real-world production.

To address this problem, a research team comprising Specially Appointed Assistant Professor Chen-Chieh Liao and Professor Hideki Koike of the School of Computing, Institute of Science Tokyo (Science Tokyo), Japan, together with researchers from Cygames, Inc., Japan, has developed the Motion Style Slider, a new AI framework for continuous control of the motion styles intensity in generated human animation. Their work was presented at the European Conference on Computer Vision 2026 (ECCV 2026) held on September 10, 2026.

The proposed framework needs to learn from only two endpoints: a neutral motion and a stylized version of the same action, such as ‘neutral walking’ and ‘happy walking.’ The style intensity ‘α,’ with values such as 0, 0.5, 1.0, and 1.5, was used to represent low, medium, and high style intensities of motion. “Given these endpoints, the model generates a continuous family of motions controlled by a user-defined scalar value, ranging from neutral behavior through the target style and into stronger reactions that lie beyond the input range. This type of control matches the way artists and directors naturally ask for a motion to be 'a little more' or 'a little less' expressive,” explains Liao. Figure 1 illustrates how changing the slider value α continuously adjusts the strength of the motion style.

The researchers implemented their innovative approach within a diffusion-based motion generation framework. The system learns the ‘direction’ in a learned motion-style embedding space along which motion changes between the neutral and stylized endpoints, without relying on fixed style labels. As shown in Figure 2, information representing this style-change direction is combined with information about the motion content and the user-specified slider value before being fed into a pretrained motion-generation model.

The team evaluated the method using multiple benchmark motion datasets and an additional dataset specifically created to assess performance beyond the styles seen during training. The method was compared against established techniques like Multi-condition Motion Latent Diffusion Model and DeepMotionEditing (Aberman et al.). “Compared with existing methods, the proposed strategy achieved favorable results in the consistent control of style intensity, smooth transitions, and extrapolation to highly expressive motion,” remarks Liao. Representative examples comparing the proposed method with existing approaches are shown in Figure 3.

To further validate the framework’s output, the researchers conducted a Likert-scale user study with 11 university participants who rated the differences in style intensity among motions created by the Motion Style Slider as clearly distinguishable, while their naturalness remained comparable.

The Motion Style Slider provides an intuitive and practical framework that can consistently modulate motion style and intensity, transitions, and nuances of AI-generated human motion. This technology could advance animation workflows in games, films, and other forms of virtual content, letting directors and designers refine character performances according to their exact creative vision.

 

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About Institute of Science Tokyo (Science Tokyo)

Institute of Science Tokyo (Science Tokyo) was established on October 1, 2024, following the merger between Tokyo Medical and Dental University (TMDU) and Tokyo Institute of Technology (Tokyo Tech), with the mission of “Advancing science and human wellbeing to create value for and with society.”

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DOI

10.1007/978-3-032-37595-7_7

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Motion Style Slider: Endpoint-Supervised Continuous Style Control for Human Motion Diffusion

The model derives the direction of style change from the stylized and neutral motions, then feeds style information corresponding to the user-specified slider value α, together with motion-content information, into a pretrained motion-generation model.

 

Examples generated by the proposed method (OURS) and existing methods (ABERMAN, MCM-LDM) as style intensity is varied for the same motion content. The proposed method changes the motion progressively in accordance with the specified style intensity.

Credit

Chen-Chieh Liao

Wearable AI forecasts prolonged sitting in women with chronic pelvic pain

Personalized models could help time prompts to move

The Mount Sinai Hospital / Mount Sinai School of Medicine

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The illustration shows how the study used wearable data to forecast prolonged sedentary periods and inform future personalized movement reminders.

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Credit: Jegminat et al., npj Women’s Health

[New York, NY] September 30,2026—Researchers at the Icahn School of Medicine at Mount Sinai have developed an artificial intelligence (AI) approach using data from wearable devices that can forecast upcoming periods of prolonged sitting in women with chronic pelvic pain disorders.

The findings, published in the September 30 online issue of npj Women’s Health [DOI: 10.1038/s44294-026-00156-5], could inform personalized digital health tools that prompt the individual to move, such as by taking a short walk, at the right time while minimizing unnecessary alerts.

Chronic pelvic pain affects an estimated one in seven women and frequently occurs in people with conditions such as endometriosis, adenomyosis, and uterine fibroids. They are often associated with prolonged sitting because of pain, fatigue, and other symptoms that impact daily life. While regular movement can help manage symptoms and improve overall health, generic advice to "sit less and move more" often fails to account for the realities of living with these conditions.

The study shows that wearable devices may do more than count steps. Using data collected over time from wearables of women with chronic pelvic pain, the researchers developed a forecasting model that can identify when prolonged sedentary periods are likely to occur during waking hours. This could allow a future digital intervention to deliver a brief reminder to stand up or take a short walk before prolonged inactivity begins.

"Our goal was to determine whether everyday wearable devices could serve as an early-warning system for prolonged sitting in women living with chronic pelvic pain," says senior author Ipek Ensari, PhD, Assistant Professor of Artificial Intelligence and Human Health at the Icahn School of Medicine and a member of the Hasso Plattner Institute of Digital Health at Mount Sinai. "Rather than offering generic advice after the fact, we wanted to determine whether we could anticipate these moments and support people with simple, well-timed prompts that fit naturally into their daily lives."

The research team analyzed wearable data from 134 women with chronic pelvic pain disorders, primarily endometriosis, along with 61 healthy participants as a comparison group. Participants wore Fitbit devices for up to 90 days, generating minute-by-minute information about physical activity, heart rate, and sleep.

Using approximately 10 days of each participant’s data, the team trained personalized forecasting models to predict activity levels one hour ahead. They then tested whether those forecasts could identify 15-minute periods of sedentary behavior during waking hours, a timeframe that could allow a brief movement break, which the researchers called an “exercise snack.”

The work challenged the assumption that health AI must be increasingly complex. Relatively simple, interpretable models forecasted prolonged sitting as accurately as more computationally intensive deep-learning approaches evaluated in the study.

"We were surprised by how well the simplest models performed," says lead author Jannes Jegminat, PhD, a former postdoctoral research fellow at the Icahn School of Medicine at Mount Sinai. "More complex AI is not always better. Lightweight, interpretable models can accurately forecast sedentary behavior while being practical enough to run directly on a person's own device, which also helps protect privacy."

Making future tools more feasible to run directly on a person’s phone or wearable could reduce computational demands and the need to transmit sensitive data to remote servers, say the investigators.

The models also remained robust even in instances of incomplete data, which can happen when participants remove their devices or forget to synchronize them. This suggests that everyday data from wearables may support meaningful predictions under real-world conditions rather than only in controlled laboratory settings.

"This study suggests that predicting prolonged sitting is feasible, even if the individual has chronic conditions that might impact their daily routine," says Dr. Ensari. "The next step is determining whether delivering personalized movement prompts based on those predictions actually helps reduce sedentary time, improves symptoms, and enhances quality of life. Those questions will require prospective clinical trials."

The researchers believe the work could ultimately support digital health tools that feel less like constant reminders and more like a personalized coach, delivering only a small number of meaningful prompts each day while minimizing unnecessary notifications that often lead users to ignore health apps.

Beyond chronic pelvic pain, the approach may also apply to other chronic conditions in which prolonged sitting contributes to poorer health outcomes.

The research team is now working to incorporate the forecasting framework into a just-in-time adaptive intervention, which will test whether personalized, AI-guided movement prompts can reduce sedentary time and improve symptoms among women living with chronic pelvic pain disorders.

The paper is titled "Robust forecasting of sedentary bouts in chronic pelvic pain disorders for on-device learning and real-time deployment."

The authors, as listed in the journal, are Jannes Jegminat, Samia Shahnawaz, Jovita Rodrigues, Matteo Danieletto, Kyle Landell, Gabriele Campanella, Carol Ewing Garber, Zahi A. Fayad, and Ipek Ensari.

The work was funded in part by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number R01HD108263, and by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences.

For more Mount Sinai artificial intelligence news, visit https://icahn.mssm.edu/about/artificial-intelligence.  

About Mount Sinai's WindreichDepartment of Artificial Intelligence and Human Health   

Led by Girish N. Nadkarni, MD, MPH, Chair of the Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai—an international authority on the safe, effective, and ethical use of AI in health care—Mount Sinai’s Windreich Department of Artificial Intelligence and Human Health is the firstof its kind at a U.S. medical school, pioneering transformative advancements at the intersection of artificial intelligence and human health.  

The Department is committed to leveraging AI in a responsible, effective, ethical, and safe manner to transform research, clinical care, education, and operations. By bringing together world-class AI expertise, cutting-edge infrastructure, and unparalleled computational power, the department is advancing breakthroughs in multi-scale, multimodal data integration while streamlining pathways for rapid testing and translation into practice.  

The Department benefits from dynamic collaborations across Mount Sinai, including with the Hasso Plattner Institute for Digital Health at Mount Sinai—a partnership between the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany, and the Mount Sinai Health System—which complements its mission by advancing data-driven approaches to improve patient care and health outcomes.  

At the heart of this innovation is the renowned Icahn School of Medicine at Mount Sinai, which serves as a central hub for learning and collaboration. This unique integration enables dynamic partnerships across institutes, academic departments, hospitals, and outpatient centers, driving progress in disease prevention, improving treatments for complex illnesses, and elevating quality of life on a global scale.  

In 2024, the Department's innovative NutriScan AI application, developed by the Mount Sinai Health System Clinical Data Science team in partnership with Department faculty, earned Mount Sinai Health System the prestigious Hearst Health Prize. NutriScan is designed to facilitate faster identification and treatment of malnutrition in hospitalized patients. This machine learning tool improves malnutrition diagnosis rates and resource utilization, demonstrating the impactful application of AI in health care.  

For more information on Mount Sinai's Windreich Department of Artificial Intelligence and Human Health, visit: ai.mssm.edu 

About the Hasso Plattner Institute at Mount Sinai  

At the Hasso Plattner Institute for Digital Health at Mount Sinai, the tools of data science, biomedical and digital engineering, and medical expertise are used to improve and extend lives. The Institute represents a collaboration between the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany, and the Mount Sinai Health System.   

Under the leadership of Girish N. Nadkarni, MD, MPH, Director of the Hasso Plattner Institute for Digital Health at Mount Sinai, and Professor Lothar Wieler, a globally recognized expert in public health and digital transformation, they jointly oversee the partnership, driving innovations that positively impact patient lives while transforming how people think about personal health and health systems.  

The Hasso Plattner Institute for Digital Health at Mount Sinai receives generous support from the Hasso Plattner Foundation. Current research programs and machine learning efforts focus on improving the ability to diagnose and treat patients.  

About the Icahn School of Medicine at Mount Sinai

The Icahn School of Medicine at Mount Sinai is internationally renowned for its outstanding research, educational, and clinical care programs. It is the sole academic partner for the seven member hospitals* of the Mount Sinai Health System, one of the largest academic health systems in the United States, providing care to New York City’s large and diverse patient population. 

The Icahn School of Medicine at Mount Sinai offers highly competitive MD, PhD, MD-PhD, and master’s degree programs, with enrollment of more than 1,200 students. It has the largest graduate medical education program in the country, with more than 2,700 clinical residents and fellows training throughout the Health System. The Graduate School of Biomedical Sciences offers 13 degree-granting programs, conducts innovative basic and translational research, and trains more than 470 postdoctoral research fellows.

Ranked 11th nationwide in National Institutes of Health (NIH) funding, the Icahn School of Medicine at Mount Sinai is among the 90th percentile of U.S. private medical schools in Sponsored Programs Direct Expenditures per Principal Investigator, according to the Association of American Medical Colleges.  More than 6,900 scientists, educators, and clinicianswork across dozens of academic departmentsand multidisciplinary institutes with an emphasis on translational research and therapeutics. Through Mount Sinai Innovation Partners (MSIP), the Health System facilitates the real-world application and commercialization of medical breakthroughs made at Mount Sinai.

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* Mount Sinai Health System member hospitals: The Mount Sinai Hospital; Mount Sinai Brooklyn; Mount Sinai Morningside; Mount Sinai Queens; Mount Sinai South Nassau; Mount Sinai West; and New York Eye and Ear Infirmary of Mount Sinai.

 

 

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DOI

10.1038/s44294-026-00156-5

Method of Research

Data/statistical analysis

Subject of Research

People

Article Title

Robust forecasting of sedentary bouts in chronic pelvic pain disorders for on-device learning and real-time deployment

Article Publication Date

30-Sep-2026