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Artem Trotsyuk – Mitigating Unintended Consequences of AI in Biomedicine (AI PHI Affinity Group)
February 2 @ 9:00 am - 10:00 am
Thank you for coming!
The recording for this meeting is available below:
Mitigating Unintended Consequences of AI in Biomedicine
The rapid advancements of artificial intelligence (AI) in biomedical research present considerable potential for misuse, including authoritarian surveillance, data misuse, bioweapon development, increase in inequity, and abuse of privacy. A multi-pronged framework may be required to mitigate these risks, looking first to non-computational spaces, next to off-the-shelf AI solutions, then to design-specific features researchers can build into their AI. When researchers remain unable to address potential for harmful misuse, the “no” principle may be a better approach: the notion that researchers may need to forego certain veins of research when misuse risks are too great.
Presented by Dr. Artem Trotsyuk, PhD
AI Ethics and Policy Fellow, Stanford University School of Medicine
Dr. Artem A. Trotsyuk is an AI ethics and policy fellow with the Stanford Center for Biomedical Ethics. He completed his PhD in Bioengineering and Masters in Computer Science with an AI specialization at Stanford University under the supervision of Dr. Geoffrey Gurtner in the Department of Surgery. He was co-advised by Dr. Zhenan Bao in the Department of Chemical Engineering alongside Dr. Russ Altman and Dr. Michael Snyder. His thesisfocused on developing a smart bandage that implements a closed-loop AI processing system for sensing and therapeutic delivery into a wound bed. His current work focuses on evaluating unintended consequences of AI in biomedicine, and developing mitigation frameworks.
Artificial Intelligence in Cancer Research – AI PHI Affinity Group
(First Friday of each month)
This group was formed to discuss the current trends and applications of artificial intelligence in cancer research and clinical practice. The group brings together AI researchers in a variety of fields (computer science, engineering, nutrition, epidemiology radiology, etc) with clinicians and advocates. Students, trainees and faculty with any or no background in AI are encouraged to attend. The goal is to foster collaborative interactions to solve problems in cancer that were thought to be unsolvable a decade ago before the broad use of deep learning and AI in medicine.