Physical Sciences and Mathematics Commons^™

Charting A Path To The Quintuple Aim: Harnessing Ai To Address Social Determinants Of Health, Yash Shah, Zachary Goldberg, Erika Harness, David Nash

College of Population Health Faculty Papers

The Quintuple Aim seeks to improve healthcare by addressing social determinants of health (SDOHs), which are responsible for 70-80% of medical outcomes. SDOH-related concerns have traditionally been addressed through referrals to social workers and community-based organizations (CBOs), but these pathways have had limited success in connecting patients with resources. Given that health inequity is expected to cost the United States nearly USD 300 billion by 2050, new artificial intelligence (AI) technology may aid providers in addressing SDOH. In this commentary, we present our experience with using ChatGPT to obtain SDOH management recommendations for archetypal patients in Philadelphia, PA. ChatGPT identified …

Accuracy Of Machine Learning To Predict The Outcomes Of Shoulder Arthroplasty: A Systematic Review, Amir H. Karimi, Joshua Langberg, Ajith Malige, Omar Rahman, Joseph A. Abboud, Michael A. Stone

Department of Orthopaedic Surgery Faculty Papers

BACKGROUND: Artificial intelligence (AI) uses computer systems to simulate cognitive capacities to accomplish goals like problem-solving and decision-making. Machine learning (ML), a branch of AI, makes algorithms find connections between preset variables, thereby producing prediction models. ML can aid shoulder surgeons in determining which patients may be susceptible to worse outcomes and complications following shoulder arthroplasty (SA) and align patient expectations following SA. However, limited literature is available on ML utilization in total shoulder arthroplasty (TSA) and reverse TSA.

METHODS: A systematic literature review in accordance with PRISMA guidelines was performed to identify primary research articles evaluating ML's ability to …

Chatgpt Can Offer Satisfactory Responses To Common Patient Questions Regarding Elbow Ulnar Collateral Ligament Reconstruction, William Johns, Alec Kellish, Dominic Farronato, Michael G. Ciccotti, Sommer Hammoud

Rothman Institute Faculty Papers

PURPOSE: To determine whether ChatGPT effectively responds to 10 commonly asked questions concerning ulnar collateral ligament (UCL) reconstruction.

METHODS: A comprehensive list of 90 UCL reconstruction questions was initially created, with a final set of 10 "most commonly asked" questions ultimately selected. Questions were presented to ChatGPT and its response was documented. Responses were evaluated independently by 3 authors using an evidence-based methodology, resulting in a grading system categorized as follows: (1) excellent response not requiring clarification; (2) satisfactory requiring minimal clarification; (3) satisfactory requiring moderate clarification; and (4) unsatisfactory requiring substantial clarification.

RESULTS: Six of 10 ten responses were …

A Comparative Study Of Responses To Retina Questions From Either Experts, Expert-Edited Large Language Models, Or Expert-Edited Large Language Models Alone, Prashant D. Tailor, Lauren A. Dalvin, John J. Chen, Raymond Iezzi, Timothy W. Olsen, Brittni A. Scruggs, Andrew J. Barkmeier, Sophie J. Bakri, Edwin H. Ryan, Peter H. Tang, D. Wilkin Parke, Peter Belin, Jayanth Sridhar, David Xu, Ajay E. Kuriyan, Yoshihiro Yonekawa, Matthew R. Starr

Wills Eye Hospital Papers

OBJECTIVE: To assess the quality, empathy, and safety of expert edited large language model (LLM), human expert created, and LLM responses to common retina patient questions.

DESIGN: Randomized, masked multicenter study.

PARTICIPANTS: Twenty-one common retina patient questions were randomly assigned among 13 retina specialists.

METHODS: Each expert created a response (Expert) and then edited a LLM (ChatGPT-4)-generated response to that question (Expert + artificial intelligence [AI]), timing themselves for both tasks. Five LLMs (ChatGPT-3.5, ChatGPT-4, Claude 2, Bing, and Bard) also generated responses to each question. The original question along with anonymized and randomized Expert + AI, Expert, and LLM …

Evaluating The Efficacy Of Chatgpt In Navigating The Spanish Medical Residency Entrance Examination (Mir): Promising Horizons For Ai In Clinical Medicine., Francisco Guillen-Grima, Sara Guillen-Aguinaga, Laura Guillen-Aguinaga, Rosa Alas-Brun, Luc Onambele, Wilfrido Ortega, Rocio Montejo, Enrique Aguinaga-Ontoso, Paul Barach, Ines Aguinaga-Ontoso

Department of Medicine Faculty Papers

UNLABELLED: The rapid progress in artificial intelligence, machine learning, and natural language processing has led to increasingly sophisticated large language models (LLMs) for use in healthcare. This study assesses the performance of two LLMs, the GPT-3.5 and GPT-4 models, in passing the MIR medical examination for access to medical specialist training in Spain. Our objectives included gauging the model's overall performance, analyzing discrepancies across different medical specialties, discerning between theoretical and practical questions, estimating error proportions, and assessing the hypothetical severity of errors committed by a physician.

MATERIAL AND METHODS: We studied the 2022 Spanish MIR examination results after excluding …

Artificial Intelligence Frameworks To Detect And Investigate The Pathophysiology Of Spaceflight Associated Neuro-Ocular Syndrome (Sans), Joshua Ong, Ethan Waisberg, Mouayad Masalkhi, Sharif Amit Kamran, Kemper Lowry, Prithul Sarker, Nasif Zaman, Phani Paladugu, Alireza Tavakkoli, Andrew G Lee

Student Papers, Posters & Projects

Spaceflight associated neuro-ocular syndrome (SANS) is a unique phenomenon that has been observed in astronauts who have undergone long-duration spaceflight (LDSF). The syndrome is characterized by distinct imaging and clinical findings including optic disc edema, hyperopic refractive shift, posterior globe flattening, and choroidal folds. SANS serves a large barrier to planetary spaceflight such as a mission to Mars and has been noted by the National Aeronautics and Space Administration (NASA) as a high risk based on its likelihood to occur and its severity to human health and mission performance. While it is a large barrier to future spaceflight, the underlying …