Artificial Intelligence in Medicine

Displaying 1 - 18 of 18CSV
Nanda, S., Abaza, K., Kyaw, P. H., Frankel, R., Sardar, P., Parikh, S. A., Shyam, T., & Chatterjee, S. (2025). Evaluating the Ability of Artificial Intelligence to Address Nuanced Cardiology Subspecialty Questions: ChatGPT and CathSAP. Journal of the Society for Cardiovascular Angiography & Interventions, 4(3), 102563. https://doi.org/10.1016/j.jscai.2025.102563
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Han, S., Kang, H. S., Gimber, P., & Lim, S. (2025). Nursing Students’ Perceptions and Use of Generative Artificial Intelligence in Nursing Education. Nursing Reports, 15(2), 68. https://doi.org/10.3390/nursrep15020068
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Idnay, B., Xu, Z., Adams, W. G., Adibuzzaman, M., Anderson, N. R., Bahroos, N., Bell, D. S., Bumgardner, C., Campion, T., Castro, M., Cimino, J. J., Cohen, I. G., Dorr, D., Elkin, P. L., Fan, J. W., Ferris, T., Foran, D. J., Hanauer, D., Hogarth, M., … Peng, Y. (2025). Environment scan of generative AI infrastructure for clinical and translational science. Npj Health Systems, 2(1). https://doi.org/10.1038/s44401-024-00009-w
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Schuemie, M. J., Ostropolets, A., Zhuk, A., Korsik, U., Seo, S. I., Suchard, M. A., Hripcsak, G., & Ryan, P. B. (2025). Standardized patient profile review using large language models for case adjudication in observational research. Npj Digital Medicine, 8(1). https://doi.org/10.1038/s41746-025-01433-4
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Elias, P., Bhave, S., & Poterucha, T. J. (2025). Novel technology, novel challenges: How do we compare the performance of artificial intelligence to human experts? European Heart Journal. https://doi.org/10.1093/eurheartj/ehae760
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Breneman, A., Trager, M. H., Gordon, E. R., Mehta, A., Husain, S., Gru, A. A., & Samie, F. H. (2024). Utilization of ChatGPT to Simplify Complex Dermatopathology Reports Into Patient-Friendly Language. The American Journal of Dermatopathology. https://doi.org/10.1097/dad.0000000000002868
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Silvestri, C., Roshal, J., Shah, M., Widmann, W. D., Townsend, C., Brian, R., L’Huillier, J. C., Navarro, S. M., Lund, S., & Sathe, T. S. (2024). Evaluation of a novel large language model (LLM)-powered chatbot for oral boards scenarios. Global Surgical Education - Journal of the Association for Surgical Education, 3(1). https://doi.org/10.1007/s44186-024-00303-z
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Campbell, E. A., Bose, S., & Masino, A. J. (2024). Conceptualizing bias in EHR data: A case study in performance disparities by demographic subgroups for a pediatric obesity incidence classifier. PLOS Digital Health, 3(10), e0000642. https://doi.org/10.1371/journal.pdig.0000642
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Sathe, T. S., Roshal, J., Naaseh, A., L’Huillier, J. C., Navarro, S. M., & Silvestri, C. (2024). How I GPT It: Development of Custom Artificial Intelligence (AI) Chatbots for Surgical Education. Journal of Surgical Education, 81(6), 772–775. https://doi.org/10.1016/j.jsurg.2024.03.004
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Odu, J., Osi, K., Nguyen, L., Goldstein, A., Appel, L. J., Matsushita, K., Ojji, D., Orji, I. A., Alex-Okoh, M., Odoh, D., Toma, M. M., Elemuwa, C. O., Lamorde, S., Baraya, H., Dewan, M. T., Chijioke, O., Moran, A. E., Agogo, E., & Thomas, M. P. (2024). On-demand mobile hypertension training for primary health care workers in Nigeria: a pilot study. BMC Health Services Research, 24(1). https://doi.org/10.1186/s12913-024-10693-x
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Choudhury, A., Elkefi, S., & Tounsi, A. (2024). Exploring factors influencing user perspective of ChatGPT as a technology that assists in healthcare decision making: A cross sectional survey study. PLOS ONE, 19(3), e0296151. https://doi.org/10.1371/journal.pone.0296151
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Columbia Affiliation
Spotnitz, M., Idnay, B., Gordon, E. R., Shyu, R., Zhang, G., Liu, C., Cimino, J. J., & Weng, C. (2024). A Survey of Clinicians’ Views of the Utility of Large Language Models. Applied Clinical Informatics, 15(02), 306–312. CLOCKSS. https://doi.org/10.1055/a-2281-7092
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Clelland, C. L., Moss, S., & Clelland, J. D. (2024). Warning: Artificial intelligence chatbots can generate inaccurate medical and scientific information and references. Exploration of Digital Health Technologies, 1–6. Portico. https://doi.org/10.37349/edht.2024.00006
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Faierstein, K. F., Manor, U. M., Fiman, M. F., Loutati, R. L., Am-Shalom, A. A., Cohen-Shelly, M. C., Rubin, N. R., Klempfner, R. K., Schwammenthal, E. S., Lotan, D. L., Blank, N. B., Raanani, E. R., & Maor, E. M. (2023). Echocardiographic age and sex estimation using artificial intelligence predicts overall survival. European Heart Journal, 44(Supplement_2). https://doi.org/10.1093/eurheartj/ehad655.001
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Avram, M. J., Culley, D. J., Davidson, A., Kharasch, E. D., Kheterpal, S., London, M. J., & Melo, M. F. V. (2022). Journal-related Activities and Other Special Activities at the 2022 American Society of Anesthesiologists Meeting. Anesthesiology, 137(4), 387–395. https://doi.org/10.1097/aln.0000000000004351
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Butler, C. R., Appelbaum, P. S., Ascani, H., Aulisio, M., Campbell, C. E., de Boer, I. H., Dighe, A. L., Hall, D. E., Himmelfarb, J., Knight, R., Mehl, K., Murugan, R., Rosas, S. E., Sedor, J. R., O’Toole, J. F., Tuttle, K. R., Waikar, S. S., Freeman, M., Alexandrov, T., … Zhang, G. (2022). A Participant-Centered Approach to Understanding Risks and Benefits of Participation in Research Informed by the Kidney Precision Medicine Project. American Journal of Kidney Diseases, 80(1), 132–138. https://doi.org/10.1053/j.ajkd.2021.10.006
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Kampaltsis, P., Emfiezoglou, M., Siouras, A., Eynde, J., Moustakidis, S., Doulamis, I. P., Duque, E. R., & Briasoulis, A. (2022). Prediction of 1-Year Mortality After Heart Transplantation in Adults with Congenital Heart Disease with Machine Learning Models. The Journal of Heart and Lung Transplantation, 41(4), S436. https://doi.org/10.1016/j.healun.2022.01.1102
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