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TNP9SLZ7
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https://tinglongdai.com/wp-content/plugins/zotpress/
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Lee, S.-H., Dai, T., Phan, P. H., Moran, N., & Stonemetz, J. (2022). The Association between Timing of Elective Surgery Scheduling and Operating Theater Utilization: A Cross-Sectional Retrospective Study. Anesthesia & Analgesia, 134(3), 455–462. https://doi.org/10.1213/ane.0000000000005871
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Ahmed, M., Dai, T., Channa, R., Abràmoff, M. D., Lehmann, H. P., & Wolf, R. M. (2025). Cost-Effectiveness of AI for Pediatric Diabetic Eye Exams from a Health System Perspective. Npj Digital Medicine, 8(1). https://doi.org/10.1038/s41746-024-01382-4
Gilbert, S., Dai, T., & Mathias, R. (2025). Consternation as Congress Proposal for Autonomous Prescribing AI Coincides with the Haphazard Cuts at the FDA. Npj Digital Medicine, 8(1). https://doi.org/10.1038/s41746-025-01540-2
Sagona, M., Dai, T., Macis, M., & Darden, M. (2025). Trust in AI-Assisted Health Systems and AI’s Trust in Humans. Npj Health Systems, 2(1), 10. https://doi.org/10.1038/s44401-025-00016-5
Dai, T., Kvedar, J. C., & Polsky, D. (2025). Policy Brief: Ambient AI Scribes and the Coding Arms Race. Npj Digital Medicine, 8(1), 780. https://doi.org/10.1038/s41746-025-02272-z
Socal, M. P., Dada, M., & Dai, T. (2025). Prescription for Made in America? Tariffs and U.S. Drug Manufacturing. Health Affairs Scholar, 3(7), qxaf122. https://doi.org/10.1093/haschl/qxaf122
Hunt, M. S., Dai, T., & Abràmoff, M. D. (2026). Evaluating Commercial Multimodal AI for Diabetic Eye Screening and Implications for an Alternative Regulatory Pathway. Npj Digital Medicine, 9(1), 42. https://doi.org/10.1038/s41746-025-02216-7
Yang, H., Dai, T., & Wolf, R. M. (2026). Financial Incentives Increase Uptake and Perceived Effectiveness of Autonomous Medical AI, Yet Patients Still Seek Human Reconfirmation. Npj Digital Medicine. https://doi.org/10.1038/s41746-026-02635-0
Gilbert, S., & Dai, T. (2026). Expert Perspectives on Recent US Digital Medicine Regulation Policy Changes and the TEMPO Pilot. Npj Digital Medicine, 9(1), 380. https://doi.org/10.1038/s41746-026-02786-0
Gilbert, S., & Dai, T. (2026). Expert Perspectives on US Regulatory Approaches to Large Language Models in Healthcare. Npj Digital Medicine, 9(1), 382. https://doi.org/10.1038/s41746-026-02787-z
Gilbert, S., & Dai, T. (2026). Expert Perspectives on the Ecosystem of Medical AI Oversight in the GenAI Era. Npj Digital Medicine, 9(1), 395. https://doi.org/10.1038/s41746-026-02785-1
Zhang, M., Wang, G., & Dai, T. (2026). The Spillover Effect of Suspending Nonessential Surgery: Evidence from Kidney Transplantation. Management Science. https://doi.org/10.1287/mnsc.2023.03624
Zuo, R., Dai, T., & Keppo, J. (2026). Incentive Design and Pricing under Limited Inventory. Manufacturing & Service Operations Management. https://doi.org/10.2139/ssrn.3989971
Cohen, M. C., Dai, T., Perakis, G., Agrawal, N., Allon, G., Boute, R. N., Cachon, G. P., Chen, Z., Cohen, M., Cristian, R., Deshpande, V., De Véricourt, F., Fransoo, J. C., Gijsbrechts, J., Harsha, P., Hu, M., Keskinocak, P., Kwon, C., Lee, H., … Zhang, D. (2026). OM Forum—Supply Chain Management in the AI Era: A Vision Statement from the Operations Management Community. Manufacturing & Service Operations Management, 28(3), 687–705. https://doi.org/10.1287/msom.2025.1065
Patil, S. V., Myers, C. G., & Dai, T. (2026). Protecting Clinical Value Judgment in the Age of AI. Npj Digital Medicine, 9(1), 269. https://doi.org/10.1038/s41746-026-02561-1
Luan, S., Singh, S., & Dai, T. (2026). Algorithm Design and Physician Liability [Working Paper]. Johns Hopkins University. https://doi.org/10.2139/ssrn.5046254
Patil, S., Dai, T., & Myers, C. G. (2026). The Hidden Costs of Outsourcing Judgment to AI Suppliers. California Management Review Insights. https://cmr.berkeley.edu/assets/documents/pdf/2026-03-the-hidden-costs-of-outsourcing-judgment-to-ai-suppliers.pdf
Zhong, H., Wang, G., & Dai, T. (2026). Wheels on the Bus: Impact of Vaccine Rollouts on Demand for Public Transportation. Production and Operations Management, 35(3), 799–816. https://doi.org/10.1177/10591478251377162
Wiberg, H., Dai, T., Lam, H., & Kulkarni, R. (2026). Synergizing Artificial Intelligence and Operations Research: Perspectives from INFORMS Fellows on the Next Frontier. INFORMS Journal on Data Science, 5(1), 14–23. https://doi.org/10.1287/ijds.2025.0077
Lee, B., Jay, M., Fox, H., Padley, J., Dai, T., & Levin, A. S. (2026). FDA-Cleared Artificial Intelligence Medical Devices in Orthopaedic Surgery. JAAOS: Global Research and Reviews, 10(2), e25.00170. https://doi.org/10.5435/JAAOSGlobal-D-25-00170
Socal, M. P., Sun, Y., Ballreich, J., Acha, J., Yazdi, M. A., Dai, T., & Dada, M. (2026). Potential Impact of Tariffs on Active Pharmaceutical Ingredients on the Price of U.S.-Made Generic Drugs. Health Affairs Scholar, 4(2), qxaf247. https://doi.org/10.1093/haschl/qxaf247
Dai, T., & Swaminathan, J. M. (2025). Artificial Intelligence and Operations: A Foundational Framework of Emerging Research and Practice. Production and Operations Management, (forthcoming). https://doi.org/10.1177/10591478251412943
Dai, T., & Singh, S. (2025). Using Artificial Intelligence as Gatekeeper or Second Opinion: Designing Patient Pathways for Artificial Intelligence Augmented Healthcare. Production and Operations Management, (forthcoming). https://doi.org/10.1177/10591478251403269
Patel, S., Sparks, S., Dong, D., Dai, T., & Lee, B. (2025). Emergence and Applications of FDA-Cleared Artificial Intelligence in Cardiovascular Care. Circulation, 152(Suppl_3). https://doi.org/10.1161/circ.152.suppl_3.4345594
Dai, T., & Taylor, T. (2025). Designing Enterprise AI Systems: Hallucination, Creativity, and Moral Hazard. https://doi.org/10.2139/ssrn.5996714
Martagan, T., & Dai, T. (2025). Synergizing Artificial Intelligence and Operations Research for Advancements in Biomanufacturing. Health Care Management Science, 28(4), 930–935. https://doi.org/10.1007/s10729-025-09725-7
Dai, T., & Tayur, S. (Eds.). (2018). Handbook of Healthcare Analytics: Theoretical Minimum for Conducting 21st Century Research on Healthcare Operations. Wiley. https://doi.org/10.1002/9781119300977
Cohen, M. C., & Dai, T. (Eds.). (2026). AI in Supply Chains: Perspectives from Global Thought Leaders (1st ed. 2026). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-07054-8
Simchi-Levi, D., Dai, T., Menache, I., & Wu, M. X. (2025). Democratizing Optimization with Generative AI [Working Paper]. Johns Hopkins University. https://doi.org/10.2139/ssrn.5511218