Humans, not AI, are always accountable for healthcare decisions, say experts at QF’s WISH 2024
A panel of bioethicists and legal experts discussed the ethical implications of AI in healthcare at the global conference in Doha
WISH/QF
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Citation: (Left to Right); UK Health Minister Andrew Gwynne; Dr. Barry Solaiman, Assistant Professor of Law at HBKU; Dr Tamar Schiff, Post-Doctoral Fellow at the Division of Medical Ethics at NYU; Dr Hans Henri P. Kluge, WHO Regional Director, EURO; Dr. Mohammed Ghaly, Professor of Islam and Bioethics at the Centre for Islamic Legislation and Ethics at Qatar Foundation’s Hamad Bin Khalifa University; along with the moderator
view moreCredit: World Innovation Summit for Health
14 November 2024. Doha, Qatar — A panel of bioethicists, legal and policy experts at the World Innovation Summit for Health (WISH) discussed the use of Artificial Intelligence (AI) in healthcare, focussing on accountability and the importance of including diverse data sets.
The discussion on the ethics of AI in healthcare, held at the end of the summit’s first day, was based on the report ‘AI and Healthcare Ethics in the Gulf Region: An Islamic Perspective on Medical Accountability’. This session featured the report’s lead author Dr. Mohammed Ghaly, Professor of Islam and Bioethics at the Centre for Islamic Legislation and Ethics at Qatar Foundation’s Hamad Bin Khalifa University (HBKU).
Prof Ghaly said that accountability for the outcomes of decisions of AI-enabled medical technologies needs to lay squarely with people: “It doesn’t matter how smart the machine is, it cannot be responsible for any mistakes.” He added that responsibility will no longer lie with the physician alone. Instead, we need to also hold developers, programmers and data scientists to account for data bias. “We are moving into a world of collective liability,” Ghaly said.
The panel highlighted several challenges to the ethical management of AI-enabled medical technologies. This issue of accountability is compounded by the complexity and opacity of AI algorithms, which can obscure the decision-making process and diffuse blame. Issues related to privacy and data protection, obtaining informed consent, addressing social disparities, considerations of medical consultation, and the aspects of empathy and sympathy pose additional challenges in the integration of AI.
The expert panel also included Dr. Barry Solaiman, Assistant Professor of Law at HBKU; Dr Hans Henri P. Kluge, WHO Regional Director, EURO; UK Health Minister Andrew Gwynne, Parliamentary Under Secretary of State for Public Health and Prevention; and Dr Tamar Schiff, Post-Doctoral Fellow at the Division of Medical Ethics at NYU.
The data used to train AI is central to understanding “what biases are baked into it,” said Dr. Schiff. In order to develop AI-enabled health systems that worked for everyone, the data needs to come from diverse data sets, the panel agreed.
While much of existing data sets currently being used to train AI in healthcare is based on data gathered in Western countries, “There are well resourced countries that can produce their own data and come to the table. We need entities from around the world to contribute,” said Dr. Ghaly.
This year, WISH was opened in the presence of Her Highness Sheikha Moza bint Nasser, Chairperson of Qatar Foundation and founder of WISH. The opening ceremony, held at Qatar National Convention Centre in Doha, included speeches from Her Excellency Dr. Hanan Mohamed Al Kuwari, Qatar’s former Minister of Public Health; Lord Darzi of Denham, Executive Chair of WISH; and Christos Christou, President of Médecins Sans Frontières.
The theme of WISH 2024 is ‘Humanizing Health: Conflict, Equity and Resilience’. It aims to highlight the need for innovation in health to support everyone, leaving nobody behind and building resilience, especially among vulnerable societies and in areas of armed conflict.
Ahead of the summit, WISH entered into a strategic partnership with the World Health Organization (WHO), collaborating on the development of a series of evidence-based reports and policy papers, as well as working with the United Nations’ health agency to develop a post-summit implementation strategy.
The summit features more than 200 experts in health speaking about evidence-based ideas and practices in healthcare innovation to address the world’s most urgent global health challenges.
AI method can spot potential disease faster, better than humans
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Traditionally, researchers and medical professionals identify pathology, or signs of disease, by analyzing and annotating tissues under a microscope like the image of testis (tubule atrophy) disease pictured here -- a process that can take many hours for one slide or image. The “deep learning” AI model developed at WSU was able to identify pathologies much faster than humans, sometimes catching signs that human pathologists had missed.
view moreCredit: Courtesy of Eric Nilsson, Skinner Laboratory, Washington State University
PULLMAN, Wash. – A “deep learning” artificial intelligence model developed at Washington State University can identify pathology, or signs of disease, in images of animal and human tissue much faster, and often more accurately, than people.
The development, detailed in Scientific Reports, could dramatically speed up the pace of disease-related research. It also holds potential for improved medical diagnosis, such as detecting cancer from a biopsy image in a matter of minutes, a process that typically takes a human pathologist several hours.
“This AI-based deep learning program was very, very accurate at looking at these tissues,” said Michael Skinner, a WSU biologist and co-corresponding author on the paper. “It could revolutionize this type of medicine for both animals and humans, essentially better facilitating these kinds of analysis.”
To develop the AI model, computer scientists Colin Greeley, a former WSU graduate student, and his advising professor Lawrence Holder trained it using images from past epigenetic studies conducted by Skinner’s laboratory. These studies involved molecular-level signs of disease in kidney, testes, ovarian and prostate tissues from rats and mice. The researchers then tested the AI with images from other studies, including studies identifying breast cancer and lymph node metastasis.
The researchers found that the new AI deep learning model not only correctly identified pathologies quickly but did so faster than previous models – and in some cases found instances that a trained human team had missed.
“I think we now have a way to identify disease and tissue that is faster and more accurate than humans,” said Holder, a co-corresponding author on the study.
Traditionally, this type of analysis required painstaking work by teams of specially trained people who examine and annotate tissue slides using a microscope—often checking each other’s work to reduce human error.
In Skinner’s research on epigenetics, which involves studying changes to molecular processes that influence gene behavior without changing the DNA itself, this analysis could take a year or even more for large studies. Now with the new AI deep learning model, they can get the same data within a couple weeks, Skinner said.
Deep learning is an AI method that attempts to mimic the human brain, a method that goes beyond traditional machine learning, Holder said. Instead, a deep learning model is structured with a network of neurons and synapses. If the model makes a mistake, it “learns” from it, using a process called backpropagation, making a bunch of changes throughout its network to fix the error, so it will not repeat it.
The research team designed the WSU deep learning model to handle extremely high-resolution, gigapixel images, meaning they contain billions of pixels. To deal with the large file sizes of these images, which can slow down even the best computer, the researchers designed the AI model to look at smaller, individual tiles but still place them in context of larger sections but in lower resolution, a process that acts sort of like zooming in and out on a microscope.
This deep learning model is already attracting other researchers, and Holder’s team is currently collaborating with WSU veterinary medicine researchers on diagnosing disease in deer and elk tissue samples.
The authors also point to the model’s potential for improving research and diagnosis in humans particularly for cancer and other gene-related diseases. As long as there is data, such as annotated images identifying cancer in tissues, researchers could train the AI model to do that work, Holder said.
“The network that we’ve designed is state-of-the-art,” Holder said. “We did comparisons to several other systems and other data sets for this paper, and it beat them all.”
This study received support from the John Templeton Foundation. Eric Nilsson, a WSU research assistant professor in the School of Biological Sciences, is also a co-author on this paper.
Journal
Scientific Reports
Article Title
Scalable deep learning artificial intelligence histopathology slide analysis and validation
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