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SUMMARY:AI in Healthcare - Opportunities and Challenges - Prof Radek Bukow
 ski - University of Texas at Austin
DTSTART:20250402T140000Z
DTEND:20250402T150000Z
UID:TALK229615@talks.cam.ac.uk
CONTACT:Andreas Bedorf
DESCRIPTION:AI offers significant opportunities in healthcare while facing
  substantial challenges. The opportunities and challenges are best appreci
 ated from a time perspective. In the short term\, the opportunities are li
 kely in the individualization of testing and interventions. AI tools also 
 show substantial promise in assisting in the interpretation of images\, ra
 diologic or histopathologic\, potentially increasing the accuracy and rapi
 dity of those tests. Recent advances show AI's potential for improving hea
 lthcare efficiency by automating administrative tasks. \n\nIn the long-ter
 m\, the opportunities are less clear but potentially very exciting. AI has
  the potential in the process of ever-increasing information synthesis to 
 achieve an individualized medical decision-making system. In such a system
 \, the vast and varied digital information created by each of us flows int
 o an ever more individualized and nuanced model\, the digital twin\, which
 \, by comparison with other digital twins\, can be used to simulate differ
 ent futures and make optimal medical decisions.\n\nAlthough very desirable
  to patients and clinicians\, such a system might be far away on the horiz
 on\, and in the meantime\, AI faces many challenges in healthcare today. T
 he main ones are related to various aspects of privacy concerns\, the pote
 ntial for propagating inequalities in healthcare\, inadvertent disruption 
 of the current workflow during the integration of AI tools\, and concerns 
 of developing over-reliance on AI tools in medical decision-making resulti
 ng in atrophy of clinicians' own decision-making skills.\nThe long-term ch
 allenge emerging with the broad use of AI is the data sources AI relies on
 . The data used by AI is "content that is accessible to the public without
  any proprietary restrictions or privacy concerns." In consequence\, the d
 ata used by AI is increasingly the data generated by itself and any bias i
 n such a positive feedback mechanism will be exponentially propagated by t
 he AI.\n\nConsidering those challenges\, initial attempts toward the digit
 al twin simulation-based medical decision-making are promising. Examples a
 re: On the individual patient level\, evidence synthesis informing individ
 ual decision-making. On the healthcare system level\, system dynamic model
 s informing healthcare policy decision-making.\n\nBio: Radek Bukowski is a
  doctor\, academic physician\, scientist and an inventor. He is the direct
 or of Computational Health & Medicine Initiatives at the Texas Advanced Co
 mputing Center\, at University of Texas at Austin.\n\nProf Bukowski is mos
 t known for his works in the fields of computational medicine\, preterm bi
 rth\, maternal fetal\, and neonatal mortality and morbidity and fetal grow
 th abnormalities. His works have been published in New England Journal of 
 Medicine and American Journal of Obstetrics and Gynecology. He is also the
  recipient of 2008 March of Dimes Award for his research in prematurity.
LOCATION:https://cam-ac-uk.zoom.us/j/87922475493?pwd=jAe4pxUSrczydAzxzahBm
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