Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study.

Artificial intelligence (AI) is rapidly being introduced into the clinical workflow of many specialties. Despite the need to train physicians who understand the utility and implications of AI and mitigate a growing skills gap, no established consensus exists on how to best introduce AI concepts to medical students during preclinical training. This study examined the effectiveness of a pilot Digital Health Scholars (DHS) non-credit enrichment elective that paralleled the Dartmouth Geisel School of Medicine's first-year preclinical curriculum with a focus on introducing AI algorithms and their applications in the concurrently occurring systems-blocks. From September 2022 to March 2023, ten self-selected first-year students enrolled in the elective curriculum run in parallel with four existing curricular blocks (Immunology, Hematology, Cardiology, and Pulmonology). Each DHS block consisted of a journal club, a live-coding demonstration, and an integration session led by a researcher in that field. Students' confidence in explaining the content objectives (high-level knowledge, implications, and limitations of AI) was measured before and after each block and compared using Mann-Whitney U tests. Students reported significant increases in confidence in describing the content objectives after all four blocks (Immunology: U = 4.5, p = 0.030; Hematology: U = 1.0, p = 0.009; Cardiology: U = 4.0, p = 0.019; Pulmonology: U = 4.0, p = 0.030) as well as an average overall satisfaction level of 4.29/5 in rating the curriculum content. Our study demonstrates that a digital health enrichment elective that runs in parallel to an institution's preclinical curriculum and embeds AI concepts into relevant clinical topics can enhance students' confidence in describing the content objectives that pertain to high-level algorithmic understanding, implications, and limitations of the studied models. Building on this elective curricular design, further studies with a larger enrollment can help determine the most effective approach in preparing future physicians for the AI-enhanced clinical workflow.

An Algorithmic Approach to Understanding Osteoarthritic Knee Pain.

Background: Osteoarthritic knee pain is a complex phenomenon, and multiple factors, both within the knee and external to it, can contribute to how the patient perceives pain. We sought to determine how well a deep neural network could predict osteoarthritic knee pain and other symptoms solely from a single radiograph view. Methods: We used data from the Osteoarthritis Initiative, a 10-year observational study of patients with knee osteoarthritis. We paired >50,000 weight-bearing, posteroanterior knee radiographs with corresponding Knee Injury and Osteoarthritis Outcome Score (KOOS) pain, symptoms, and activities of daily living subscores and used them to train a series of deep learning models to predict those scores solely from raw radiographic input. We created regression models for specific score predictions and classification models to predict whether the modeled KOOS subscore exceeded a range of thresholds. Results: The root-mean-square errors were 15.7 for KOOS pain, 13.1 for KOOS symptoms, and 14.2 for KOOS activities of daily living. Modeling was performed to predict whether pain was above or below given pain thresholds, and was able to predict extreme pain (KOOS pain < 40) with an area under the curve (AUC) of 0.78. Notably, the system was also able to correctly predict numerous cases where the Kellgren-Lawrence (KL) grade assigned by the radiologist was 0 but patient pain was high, and cases where the KL grade was 4 but patient pain was low. Conclusions: A deep neural network can be trained to predict the osteoarthritic knee pain that a patient experienced and other symptoms with reasonable accuracy from a single posteroanterior view of the knee, even using low-resolution images. The system can predict pain and dysfunction that the traditional KL grade does not capture. Deep learning applied to raw imaging inputs holds promise for disentangling sources of pain within the knee from aggravating factors external to the knee. Level of Evidence: Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.