This event is a BCHSI Special Invited Seminar.
Artificial Intelligence for Clinical Decision Making Across Modalities
The Model Is Not the Problem
The Implementation and Evaluation of AI in Real-World Clinical Settings Seminar Series is cosponsored by: UCSF Bakar Institute for Computational Health Science, UCSF Division of Clinical Informatics and Digital Transformation, Department of Medicine, UCSF and UC Berkeley Computational Precision Health Program, and UCSF Department of Epidemiology and Biostatistics.
Narang, K., Addo, N., Williams, C. Y. K., Firnberg, M., Feng, J., & Kornblith, A. (2026). Assessing acuity in pediatric emergency department triage: Performance of a large language model. npj Health Systems, 3, Article 68. https://doi.org/10.1038/s44401-026-00128-6
Narang, K., Addo, N., Williams, C. Y. K., Firnberg, M., Feng, J., & Kornblith, A. (2026). Assessing acuity in pediatric emergency department triage: Performance of a large language model. npj Health Systems, 3, Article 68. https://doi.org/10.1038/s44401-026-00128-6
Kishore, S. P., Narang, P., & Auerbach, A. (2026). Sorting results of unknown significance—A framework for clinicians navigating wearable data in the AI era. JAMA, 336(9), 733–735. https://doi.org/10.1001/jama.2026.13895
Kishore, S. P., Narang, P., & Auerbach, A. (2026). Sorting results of unknown significance—A framework for clinicians navigating wearable data in the AI era. JAMA, 336(9), 733–735. https://doi.org/10.1001/jama.2026.13895
Schubert, A., Liang, M. Q., Sagers, L., & Obermeyer, Z. (2026). Deep-learning analysis of 12-lead ECGs detects drug-induced hERG inhibition and improves risk stratification beyond QTc. European Heart Journal Supplements, 28(Supplement_8), suag097.224. https://doi.org/10.1093/eurheartjsupp/suag097.224
Schubert, A., Liang, M. Q., Sagers, L., & Obermeyer, Z. (2026). Deep-learning analysis of 12-lead ECGs detects drug-induced hERG inhibition and improves risk stratification beyond QTc. European Heart Journal Supplements, 28(Supplement_8), suag097.224. https://doi.org/10.1093/eurheartjsupp/suag097.224
Bellucci, G., Gu, W., Rose, P. W., & Baranzini, S. E. (2026). An agentic AI framework connecting language models to electronic health records and a biomedical knowledge graph for real-world evidence. Frontiers in Artificial Intelligence, 9, Article 1883853. https://doi.org/10.3389/frai.2026.1883853
Bellucci, G., Gu, W., Rose, P. W., & Baranzini, S. E. (2026). An agentic AI framework connecting language models to electronic health records and a biomedical knowledge graph for real-world evidence. Frontiers in Artificial Intelligence, 9, Article 1883853. https://doi.org/10.3389/frai.2026.1883853
Fukuoka, Y., Kim, D. D., Zhang, J., Hoffmann, T. J., & Sagae, K. (2026). Short-term efficacy of the artificial intelligence HeartBot II in increasing awareness and knowledge of heart attack in women: Protocol for a randomized controlled trial with a waitlist control. JMIR Research Protocols, 15, Article e103597. https://doi.org/10.2196/103597
Fukuoka, Y., Kim, D. D., Zhang, J., Hoffmann, T. J., & Sagae, K. (2026). Short-term efficacy of the artificial intelligence HeartBot II in increasing awareness and knowledge of heart attack in women: Protocol for a randomized controlled trial with a waitlist control. JMIR Research Protocols, 15, Article e103597. https://doi.org/10.2196/103597
Gambini, L., Lau, C., Moon-Grady, A.J., Zhao, Y., Akintoye, O., Belay, W., Eckersley, L., Fatusin, O., Chambers Gurson, S., Harrington, J., Howley, L., Pinto, N., Ramlogan, S., Srinivasan, R., Wang, S. and Arnaout, R. (2026), OP14.01: Toward multicenter generalizability for deep learning-enabled congenital heart disease detection: Fetal Heart Society collaborative study. Ultrasound Obstet Gynecol, 68: 78-79. https://doi.org/10.1002/uog.70305_201
Gambini, L., Lau, C., Moon-Grady, A.J., Zhao, Y., Akintoye, O., Belay, W., Eckersley, L., Fatusin, O., Chambers Gurson, S., Harrington, J., Howley, L., Pinto, N., Ramlogan, S., Srinivasan, R., Wang, S. and Arnaout, R. (2026), OP14.01: Toward multicenter generalizability for deep learning-enabled congenital heart disease detection: Fetal Heart Society collaborative study. Ultrasound Obstet Gynecol, 68: 78-79. https://doi.org/10.1002/uog.70305_201
Elia, M. V., Friesner, I. D., Kwon, D., Ni, L., Sinha, S., Ishiyama, Y., Zack, T., Bridge, M., Fong, L., & Hong, J. C. (2026). Large language models and adverse event detection within immunotherapy clinical trials. JAMA Network Open, 9(9), Article e2631840. https://doi.org/10.1001/jamanetworkopen.2026.31840
Elia, M. V., Friesner, I. D., Kwon, D., Ni, L., Sinha, S., Ishiyama, Y., Zack, T., Bridge, M., Fong, L., & Hong, J. C. (2026). Large language models and adverse event detection within immunotherapy clinical trials. JAMA Network Open, 9(9), Article e2631840. https://doi.org/10.1001/jamanetworkopen.2026.31840
