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Full-Text Articles in Law
Introduction: Medical-Legal Partnerships: Equity, Evolution, And Evaluation., Katherine L. Kraschel, James Bhandary-Alexander, Yael Z. Cannon, Vicki W. Girard, Abbe R. Gluck, Jennifer L. Huer, Medha D. Makhlouf
Introduction: Medical-Legal Partnerships: Equity, Evolution, And Evaluation., Katherine L. Kraschel, James Bhandary-Alexander, Yael Z. Cannon, Vicki W. Girard, Abbe R. Gluck, Jennifer L. Huer, Medha D. Makhlouf
Faculty Scholarly Works
The COVID-19 pandemic laid bare systemic inequities shaped by social determinants of health (SDoH). Public health agencies, legislators, health systems, and community organizations took notice, and there is currently unprecedented interest in identifying and implementing programs to address SDoH. This special issue focuses on the role of medical-legal partnerships (MLPs) in addressing SDoH and racial and social inequities, as well as the need to support these efforts with evidence-based research, data, and meaningful partnerships and funding.
Video Endoscopy As Big Data: Balancing Privacy And Progress In Gastroenterology, Eugenia N. Uche-Anya, Sara Gerke, Tyler M. Berzin
Video Endoscopy As Big Data: Balancing Privacy And Progress In Gastroenterology, Eugenia N. Uche-Anya, Sara Gerke, Tyler M. Berzin
Faculty Scholarly Works
Tens of millions of gastrointestinal (GI) endoscopy videos and images are generated annually in the United States (1). A single 15-minute endoscopic procedure, recorded at 30 frames per second, generates approximately 27,000 high-definition images, representing a treasure trove of potential data. In the era of artificial intelligence (AI) and machine learning (ML), this data stream will not only fuel innovative and clinically impactful research in gastroenterology for both academic and commercial purposes, but also introduce ethical and legal concerns that merit consideration. Gastroenterologists are now faced with navigating new questions around data privacy and data ownership that have …
Decoding U.S. Tort Liability In Healthcare's Black-Box Ai Era: Lessons From The European Union, Mindy Duffourc, Sara Gerke
Decoding U.S. Tort Liability In Healthcare's Black-Box Ai Era: Lessons From The European Union, Mindy Duffourc, Sara Gerke
Faculty Scholarly Works
The rapid development of sophisticated artificial intelligence (“AI”) tools in healthcare presents new possibilities for improving medical treatment and general health. Currently, such AI tools can perform a wide range of health-related tasks, from specialized autonomous systems that diagnose diabetic retinopathy to general-use generative models like ChatGPT that answer users’ health-related questions. On the other hand, significant liability concerns arise as medical professionals and consumers increasingly turn to AI for health information. This is particularly true for black-box AI because while potentially enhancing the AI’s capability and accuracy, these systems also operate without transparency, making it difficult or even impossible …
State Flexibility In Emergency Medicaid To Care For Uninsured Noncitizens, Jin K. Park, Clarisa Reyes-Becerra, Medha D. Makhlouf
State Flexibility In Emergency Medicaid To Care For Uninsured Noncitizens, Jin K. Park, Clarisa Reyes-Becerra, Medha D. Makhlouf
Faculty Scholarly Works
No abstract provided.
How Ai Can Learn From The Law: Putting Humans In The Loop Only On Appeal, I. Glenn Cohen, Boris Babic, Sara Gerke, Qiong Xia,, Theodoros Evgeniou, Klaus Wertenbroch
How Ai Can Learn From The Law: Putting Humans In The Loop Only On Appeal, I. Glenn Cohen, Boris Babic, Sara Gerke, Qiong Xia,, Theodoros Evgeniou, Klaus Wertenbroch
Faculty Scholarly Works
While the literature on putting a “human in the loop” in artificial intelligence (AI) and machine learning (ML) has grown significantly, limited attention has been paid to how human expertise ought to be combined with AI/ML judgments. This design question arises because of the ubiquity and quantity of algorithmic decisions being made today in the face of widespread public reluctance to forgo human expert judgment. To resolve this conflict, we propose that human expert judges be included via appeals processes for review of algorithmic decisions. Thus, the human intervenes only in a limited number of cases and only after an …
Stemming The Shadow Pandemic: Integrating Sociolegal Services In Contact Tracing And Beyond, Medha D. Makhlouf
Stemming The Shadow Pandemic: Integrating Sociolegal Services In Contact Tracing And Beyond, Medha D. Makhlouf
Faculty Scholarly Works
The COVID-19 pandemic has shed light on the challenges of complying with public health guidance to isolate or quarantine without access to adequate income, housing, food, and other resources. When people cannot safely isolate or quarantine during an outbreak of infectious disease, a critical public health strategy fails. This article proposes integrating sociolegal needs screening and services into contact tracing as a way to mitigate public health harms and pandemic-related health inequities.