Introduction

• News How AI Can Boost Cancer, Depression and Perioperative Care
• News AI for Health Institute Launches to Promote Growing Intersection of Artificial Intelligence, Health
• News Wearable Fitness Trackers Help Physicians Track Patient Health
• News Award for paper on predicting postoperative complications with wearables, AI
• News Data from Wearables Could Be a Boon to Mental Health Diagnosis
• News What Your Fitness Tracker Says About Your Mental Health
• News Personalized Prediction of Depression Treatment Outcomes with Wearables

Clinically Meaningful Machine Learning

• S.S. Lou, H. Liu, C. Lu, T.S. Wildes, B.L. Hall, and T. Kannampallil, Personalized Surgical Transfusion Risk Prediction Using Machine Learning to Guide Preoperative Type and Screen Orders, Anesthesiology, Vol. 137, 55-66, July 2022. Required
• J. Wiens, S. Saria, M. Sendak, M. Ghassemi, V.X. Liu, F. Doshi-Velez, K. Jung, K. Heller, D. Kale, M. Saeed, P.N. Ossorio, S. Thadaney-Israni, and A. Goldenberg, Do no harm: a roadmap for responsible machine learning for health care, Nature Medicine, 25:1337-1340, August 2019.
• F.S. van Royen, K.G.M. Moons, G.-J. Geersing, and M. van Smeden, Developing, validating, updating and judging the impact of prognostic models for respiratory diseases, European Respiratory Journal, 60(3):2200250, September 2022.

Quality Assurance and Monitoring of Clinical AI

• National Academies of Sciences, Engineering, and Medicine, Machine Learning for Safety-Critical Applications: Opportunities, Challenges, and a Research Agenda, The National Academies Press, Washington, DC, 2025. Required (Executive Summary Only)
• O. Kilim, A. Olar, T. Joo, T. Palicz, P. Pollner, and I. Csabai, Physical imaging parameter variation drives domain shift, Scientific Reports, Vol. 12, 21302, December 2022.
• K. Drukker, W. Chen, J. Gichoya, N. Gruszauskas, J. Kalpathy-Cramer, S. Koyejo, K. Myers, R.C. Sa, B. Sahiner, H. Whitney, Z. Zhang, and M. Giger, Toward fairness in artificial intelligence for medical image analysis: Identification and mitigation of potential biases in the roadmap from data collection to model deployment, Journal of Medical Imaging, Vol. 10(6), 061104, November 2023.
• D.F. Sittig and H. Singh, Recommendations to Ensure Safety of AI in Real-World Clinical Care, JAMA, Vol. 333(6), 457-458, February 2025.

AI for Genetics

• J. Cheng, G. Novati, J. Pan, C. Bycroft, A. Zemgulyt?, T. Applebaum, A. Pritzel, L.H. Wong, M. Zielinski, T. Sargeant, R.G. Schneider, A.W. Senior, J. Jumper, D. Hassabis, P. Kohli, and Z. Avsec, Accurate proteome-wide missense variant effect prediction with AlphaMissense, Science, 381(6664):eadg7492, September 2023. Critique #1
• M. Kircher, D.M. Witten, P. Jain, B.J. O'Roak, G.M. Cooper, and J. Shendure, A general framework for estimating the relative pathogenicity of human genetic variants, Nature Genetics, 46(3):310-315, March 2014.
• G.M. Findlay, R.M. Daza, B. Martin, M.D. Zhang, A.P. Leith, M. Gasperini, J.D. Janizek, X. Huang, L.M. Starita, and J. Shendure, Accurate classification of BRCA1 variants with saturation genome editing, Nature, 562(7726):217-222, October 2018.
• C. Li, J. Wilborn, S. Pittman, J. Daw, J. Alonso-Perez, J. Diaz-Manera, C.C. Weihl, and G. Haller, Comprehensive functional characterization of SGCB coding variants predicts pathogenicity in limb-girdle muscular dystrophy type R4/2E, Journal of Clinical Investigation, 133(12):e168156, June 2023.

AI for Perioperative Care

• B.A. Fritz, C.R. King, M. Abdelhack, Y. Chen, A. Kronzer, J. Abraham, S. Tripathi, A. Ben Abdallah, T. Kannampallil, T.P. Budelier, D. Helsten, A. Montes de Oca, D. Mehta, P. Sontha, O. Higo, P. Kerby, S.H. Gregory, T.S. Wildes, and M.S. Avidan, Effect of machine learning models on clinician prediction of postoperative complications: the Perioperative ORACLE randomised clinical trial, British Journal of Anaesthesia, 133(5):1042-1050, November 2024. Required
• B.A. Fritz, S. Pugazenthi, T.P. Budelier, B.R. Tellor Pennington, C.R. King, M.S. Avidan, and J. Abraham, User-Centered Design of a Machine Learning Dashboard for Prediction of Postoperative Complications, Anesthesia & Analgesia, 138(4):804-813, April 2024.
• B.A. Fritz, Z. Cui, M. Zhang, Y. He, Y. Chen, A. Kronzer, A. Ben Abdallah, C.R. King, and M.S. Avidan, Deep-learning model for predicting 30-day postoperative mortality, British Journal of Anaesthesia, 123(5):688-695, November 2019.

AI and Social Policy and Health

• J. Huang, M. Sherraden, and Y. Zhang, The Social Contract in the Age of Intelligence: Reconstructing Inclusive Welfare through Universal Basic Assets, Journal of the Society for Social Work and Research, Just Accepted, 2026. Required

Large Language Models for Perioperative Care

• C. Alba, B. Xue, J. Abraham, T. Kannampallil, and C. Lu, The foundational capabilities of large language models in predicting postoperative risks using clinical notes, npj Digital Medicine, 8:95, February 2025. Critique #2
• News Foundation AI model predicts postoperative risks from clinical notes

AI for Surgical Risk Prediction

• J. Zhang, D. Li, R. Dai, H. Cos, G.A. Williams, L. Raper, C.W. Hammill, and C. Lu, Predicting Post-Operative Complications with Wearables: A Case Study with Patients Undergoing Pancreatic Surgery, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (UbiComp'22), 6(2), Article 87, July 2022. Critique #3
• Z. Xu, J. Zhang, S. Haroutounian, H. Liu, Z. Cao, G. R. Messner, H. B. Alaverdyan, S. Ahuja, R. Koshy, J. Hanns, M. Frumkin, T. L. Rodebaugh, and C. Lu, Incorporating Uncertainty in Predictive Models Using Mobile Sensing and Clinical Data: A Case Study on Persistent Post-surgical Pain, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 9(2), Article 58, 2025.
• Z. Xu, J. Zhang, J. Greenberg, M. Frumkin, S. Javeed, J. Zhang, B. Benedict, K. Botterbush, T. Rodebaugh, W. Ray, C. Lu, Predicting Multi-dimensional Surgical Outcomes with Multi-modal Mobile Sensing: A Case Study with Patients Undergoing Lumbar Spine Surgery, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (UbiComp'24), 8(2), June 2024.
• News New Machine Learning Method Can Better Predict Spine Surgery Outcomes

AI for Laboratory Medicine

• N.C. Spies, L. Militello, C.W. Farnsworth, J.M. El-Khoury, T.J.S. Durant, and M.A. Zaydman, Prospective and External Validation of an Ensemble Learning Approach to Sensitively Detect Intravenous Fluid Contamination in Basic Metabolic Panels, Clinical Chemistry, 71(2):296–306, February 2025. Required
• H. Hou, R. Zhang, and J. Li, Artificial intelligence in the clinical laboratory, Clinica Chimica Acta, 559:119724, June 2024.
• Association for Diagnostics & Laboratory Medicine (ADLM), Artificial Intelligence in Laboratory Medicine: Leveraging Laboratory Expertise to Improve Patient Care, ADLM Policy Brief, 2026.

Chatbots for Mental Health

• M.V. Heinz, D.M. Mackin, B.M. Trudeau, S. Bhattacharya, Y. Wang, H.A. Banta, A.D. Jewett, A.J. Salzhauer, T.Z. Griffin, and N.C. Jacobson, Randomized Trial of a Generative AI Chatbot for Mental Health Treatment, NEJM AI, 2(4), March 2025. Critique #4
• E.E. Fitzsimmons-Craft, W.W. Chan, A.C. Smith, M.L. Firebaugh, L.A. Fowler, N. Topooco, B. DePietro, D.E. Wilfley, C.B. Taylor, and N.C. Jacobson, Effectiveness of a Chatbot for Eating Disorders Prevention: A Randomized Clinical Trial, International Journal of Eating Disorders, 55(3):343-353, March 2022.
• M. Rahsepar Meadi, T. Sillekens, S. Metselaar, A. van Balkom, J. Bernstein, and N. Batelaan, Exploring the Ethical Challenges of Conversational AI in Mental Health Care: Scoping Review, JMIR Mental Health, 12:e60432, February 2025.

Mobile Health for Perioperative Care

• J.K. Greenberg, M. Frumkin, Z. Xu, J. Zhang, S. Javeed, J. Zhang, B. Benedict, K. Botterbush, S. Yakdan, C.A. Molina, B.H. Pennicooke, D. Hafez, J.I. Ogunlade, N. Pallotta, M.C. Gupta, J.M. Buchowski, B. Neuman, M. Steinmetz, Z. Ghogawala, M.P. Kelly, B.R. Goodin, J.F. Piccirillo, T.L. Rodebaugh, C. Lu, W.Z. Ray, Preoperative Mobile Health Data Improve Predictions of Recovery from Lumbar Spine Surgery, Neurosurgery, 5(3):p 548-555, September 2024. Required

Causal Machine Learning

• Bica, I., Alaa, A.M., Lambert, C. and van der Schaar, M. (2021), From Real-World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges. Clin. Pharmacol. Ther., 109: 87-100. Required
• Sanchez Pedro, Voisey Jeremy P., Xia Tian, Watson Hannah I., O'Neil Alison Q. and Tsaftaris Sotirios A. 2022. Causal machine learning for healthcare and precision medicine. R. Soc. open sci. 9220638220638.
• Christos Louizos, Uri Shalit, Joris M. Mooij, David Sontag, Richard Zemel, Max Welling. Causal Effect Inference with Deep Latent-Variable Models. NIPS 2017.

AI for Pathology

• E. Vorontsov, A. Bozkurt, A. Casson, G. Shaikovski, M. Zelechowski, K. Severson, E. Zimmermann, J. Hall, N. Tenenholtz, N. Fusi, E. Yang, P. Mathieu, A. van Eck, D. Lee, J. Viret, E. Robert, Y.K. Wang, J.D. Kunz, M.C.H. Lee, J.H. Bernhard, R.A. Godrich, G. Oakley, E. Millar, M. Hanna, H. Wen, J.A. Retamero, W.A. Moye, R. Yousfi, C. Kanan, D.S. Klimstra, B. Rothrock, S. Liu, and T.J. Fuchs, A foundation model for clinical-grade computational pathology and rare cancers detection, Nature Medicine, 30:2924-2935, July 2024. Critique #5
• D.-T. Hoang, G. Dinstag, E.D. Shulman, L.C. Hermida, D.S. Ben-Zvi, E. Elis, K. Caley, S.-J. Sammut, S. Sinha, N. Sinha, C.H. Dampier, C. Stossel, T. Patil, A. Rajan, W. Lassoued, J. Strauss, S. Bailey, C. Allen, J. Redman, T. Beker, P. Jiang, T. Golan, S. Wilkinson, A.G. Sowalsky, S.R. Pine, C. Caldas, J.L. Gulley, K. Aldape, R. Aharonov, E.A. Stone, and E. Ruppin, A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics, Nature Cancer, 5(9):1305-1317, September 2024.
• Z. Yi, C. Xi, M.C. Menon, P. Cravedi, F. Tedla, A. Soto, Z. Sun, K. Liu, J. Zhang, C. Wei, M. Chen, W. Wang, B. Veremis, M. Garcia-Barros, A. Kumar, D. Haakinson, R. Brody, E.U. Azeloglu, L. Gallon, P. O'Connell, M. Naesens, R. Shapiro, R.B. Colvin, S. Ward, F. Salem, and W. Zhang, A large-scale retrospective study enabled deep-learning based pathological assessment of frozen procurement kidney biopsies to predict graft loss and guide organ utilization, Kidney International, 105(2):281-292, February 2024.