Biography Research Focus My main research aim is to better predict and prevent complications in surgical patients using advanced machine learning techniques. My work includes: Real-Time Risk Assessment: Creating models to predict acute kidney injury (AKI), pain, delirium, and blood loss during surgeries in real-time. Model Validation: Ensuring these models are reliable with the eventual goal of pursuing regulatory approval. I strive to build models that I would want to use when caring for patients. Additionally, my research explores the space of computer-human interfaces related to artificial intelligence (AI) to seamlessly integrate these models into clinical workflows. Clinical Practice As an anesthesiologist, I am committed to providing safe and effective patient care in the operating room. My clinical work is closely linked with my research, fostering an environment where new insights directly benefit patient outcomes. Commitment to Progress I value the opportunity to contribute to meaningful advancements in healthcare. By integrating innovative technology into clinical practices, I seek to improve patient outcomes and advance the field of perioperative medicine. Role Faculty Location UCSF at Mission Bay UCSF at Mount Zion UCSF at Parnassus Category Clinical Research Education 06/2022 - Diversity, Equity, and Inclusion Champion Training, University of California06/2020 - Medical Informatics and Artificial Intelligence, Bakar Computational Health Sciences Institute at University of California, San Francisco (UCSF)D.ABA., 05/2019 - Anesthesiology, University of California, San Francisco (UCSF)MD, 05/2014 - Medicine, Harvard Medical School (HMS)BSE, 06/2009 - Mechanical Engineering, Massachusetts Institute of Technology (MIT) Publications Real-time, artificial intelligence-guided intraoperative resuscitation and fluid management in trauma anesthesia. Holt M, Shanmugam A, Bishara AM Current opinion in anaesthesiology . Feb 11, 2026 REACH-BMAS (Body-Map Auto-Segmentation): Screening Body-Map Drawings for Chronic Widespread Pain. de Rinaldis E, Hue TF, Cummings J, Peterson TA, Bailey JF, Lotz JC, Bishara A, REACH Investigators Pain medicine (Malden, Mass.) . Jan 21, 2026 A Summary of Pain Locations and Neuropathic Patterns Extracted Automatically from Patient Self-Reported Sensation Drawings. Bishara A, de Rinaldis E, Hue TF, Peterson T, Cummings J, Torres-Espin A, Bailey JF, Lotz JC International journal of environmental research and public health . Sep 19, 2025 Development of a machine learning-derived model to predict unplanned ICU admissions after major non-cardiac surgery. Chiu C, Braehler MR, Donovan AL, Butte AJ, Pirracchio R, Bishara AM BMC anesthesiology . Jul 17, 2025 Machine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database: Algorithm Development and Validation Study. Kendale S, Bishara A, Burns M, Solomon S, Corriere M, Mathis M JMIR AI . Sep 8, 2023 Development of a Machine Learning Model of Postoperative Acute Kidney Injury Using Non-Invasive Time-Sensitive Intraoperative Predictors. Zamirpour S, Hubbard AE, Feng J, Butte AJ, Pirracchio R, Bishara A Bioengineering (Basel, Switzerland) . Aug 5, 2023 Gender-based time discrepancy in diagnosis of coronary artery disease based on data analytics of electronic medical records. Panahiazar M, Bishara AM, Chern Y, Alizadehsani R, Islam SMS, Hadley D, Arnaout R, Beygui RE Frontiers in cardiovascular medicine . Nov 24, 2022 A descriptive appraisal of quality of reporting in a cohort of machine learning studies in anesthesiology. Kothari R, Chiu C, Moukheiber M, Jehiro M, Bishara A, Lee C, Pirracchio R, Celi LA Anaesthesia, critical care & pain medicine . Jul 8, 2022 Data Analytics of Electronic Health Records to Enhance Care of Coronary Artery Disease in Younger Women with Avoiding Possible Delay in Treatment. Panahiazar M, Bishara AM, Chern Y, Alizadehsani R, Latif OS, Hadley D, Beygui RE Studies in health technology and informatics . Jun 6, 2022 Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare. Feng J, Phillips RV, Malenica I, Bishara A, Hubbard AE, Celi LA, Pirracchio R NPJ digital medicine . May 31, 2022 Opioid prescribing practices at hospital discharge for surgical patients before and after the Centers for Disease Control and Prevention's 2016 opioid prescribing guideline. Langnas E, Bishara A, Croci R, Rodriguez-Monguio R, Wick EC, Chen CL, Guan Z BMC anesthesiology . May 11, 2022 Considerations for the implementation of machine learning into acute care settings. Bishara A, Maze EH, Maze M British medical bulletin . Mar 21, 2022 Postoperative delirium prediction using machine learning models and preoperative electronic health record data. Bishara A, Chiu C, Whitlock EL, Douglas VC, Lee S, Butte AJ, Leung JM, Donovan AL BMC anesthesiology . Jan 3, 2022 Opal: an implementation science tool for machine learning clinical decision support in anesthesia. Bishara A, Wong A, Wang L, Chopra M, Fan W, Lin A, Fong N, Palacharla A, Spinner J, Armstrong R, Pletcher MJ, Lituiev D, Hadley D, Butte A Journal of clinical monitoring and computing . Nov 27, 2021 Machine Learning Prediction of Liver Allograft Utilization From Deceased Organ Donors Using the National Donor Management Goals Registry. Bishara AM, Lituiev DS, Adelmann D, Kothari RP, Malinoski DJ, Nudel JD, Sally MB, Hirose R, Hadley DD, Niemann CU Transplantation direct . Sep 27, 2021 Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991-2020. Alizadehsani R, Khosravi A, Roshanzamir M, Abdar M, Sarrafzadegan N, Shafie D, Khozeimeh F, Shoeibi A, Nahavandi S, Panahiazar M, Bishara A, Beygui RE, Puri R, Kapadia S, Tan RS, Acharya UR Computers in biology and medicine . Oct 28, 2020 Differences in clinical deterioration among three sub-phenotypes of COVID-19 patients at the time of first positive test: results from a clustering analysis. Data Science Collaborative Group Intensive care medicine . Oct 19, 2020 Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database. Nudel J, Bishara AM, de Geus SWL, Patil P, Srinivasan J, Hess DT, Woodson J Surgical endoscopy . Jan 17, 2020 Reduced-gravity environment hardware demonstrations of a prototype miniaturized flow cytometer and companion microfluidic mixing technology. Phipps WS, Yin Z, Bae C, Sharpe JZ, Bishara AM, Nelson ES, Weaver AS, Brown D, McKay TL, Griffin D, Chan EY Journal of visualized experiments : JoVE . Nov 13, 2014
