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Hooman H. Rashidi MD, MS, FCAP

Associate Dean of AI in Medicine, Professor & Endowed Chair of Lombardi-Shinozuka Experimental Pathology Research at University of Pittsburgh School of Medicine, Executive Vice Chair of Computational Pathology & Informatics Division at UPMC, Executive Director of CPACE (Computational Pathology & AI Center of Excellence)

Dr. Rashidi combines his passion for patient care, research and education with his unique training in bioinformatics and machine learning (ML) to create innovative new tools (i.e. MILO, STNG, Pitt-GPT+, WSI-Genie, Nebulon-GPT, etc.) and resources (Hematology Outlines, Cleveland Clinic's AI course, etc.) that improve clinical practice, research and education.

His experience in AI/ML dates back to his graduate years at UCSD which subsequently allowed him to serve as the principal author and editor of several popular bioinformatics textbooks. This background has also enabled him to develop various novel AI-ML platforms. He is currently the Associate Dean of AI in Medicine, Professor & Endowed Chair of Lombardi-Shinozuka Experimental Pathology Research at the University of Pittsburgh School of Medicine, Executive Director of CPACE (Computational Pathology & AI Center of Excellence) and the Executive Vice Chair of the Computational Pathology & Informatics Division at UPMC. He is also Adjunct Professor of Engineering at the Carnegie Mellon University. Before joining University Pittsburgh / UPMC, he served as the founding director of Cleveland Clinic’s Center for Diagnostics & Artificial Intelligence & Vice Chair of Technology Innovation & Computational Pathology, and before that he served as the Director of AI for University of California Davis Medical Center and Professor & Vice chair of informatics, leading a large number of AI studies with numerous collaborators from various prominent institutions. These experiences and studies have also led to the invention of numerous products (most recent of which being the on-prem LLM framework Pitt-GPT+, the automated Image analytics frameworks WSI Genie and Auto-Pix AI) & several filed patents/established products (e.g. the University of California IP MILO: Machine Intelligence Learning Optimizer, the proprietary Automated ML platform & it’s suite of 6 complimentary separate unique Data Science and Statistics Apps & Cleveland Clinic’s new automated synthetic data generator and Validation platform STNG, Synthetic Tabular Neural Generator) along with a large number of manuscripts in which he serves as corresponding author. These products have also been licensed to several industry and academic institutions & serving as a powerful suite of data science tools for a large number of clinical, quality, research and educational projects while also starting to help optimize and expedite data access needs for all investigators.

In addition to the above, Dr. Rashidi is also a practicing physician (board certified hematopathologist) and a well-known educator with numerous teaching awards who has created some of the AppStore’s most popular hematology Apps along with Cleveland Clinic’s most recent AI-ML course (launched Cleveland Clinic wide in Jan 2024). He is also the co-founder and senior editor of HematologyOutlines, a very popular digital hematology atlas that is used internationally and endorsed by the American Society of Clinical Pathology.

Dr. Rashidi’s efforts in the AI-ML & digital space are internationally recognized, as evidenced by his continuous invited talks at prominent conferences & institutions, his various journal AI/ML editorial roles (e.g. Senior Editor of AI for the official journal of the United States & Canadian Academy of Pathology, “Modern Pathology” and Senior Associate Editor of Machine Learning & Computational Pathology at JPI, the official journal of the Association of Pathology Informatics), his multitude of key invited review articles (e.g. Modern Pathology’s most recent AI review article series:  https://www.modernpathology.org/article/S0893-3952(24)00253-9/fulltext ) & his continued national committee roles within this space.

Education & Training
Fellowship - UCLA (University of California, Los Angeles), School of Medicine, Hematopathology Fellow
Residency (AP/CP) - Yale School of Medicine/Yale New Haven Medical Center, Dept. of Pathology and Laboratory Medicine (AP/CP)
Residency (Internal Medicine) - Harvard Medical School/Beth Israel Deaconess Med Center
MD - University of Texas Medical School, San Antonio
MS - University of California at San Diego
BS - University of California at San Diego
Awards
Sones Innovation Award 2023 Nominee, Cleveland Clinic
Chancellor’s Innovation Award 2020 Nominee, UC Davis
Dean’s Award for Excellence in Education, UC Davis School of Med. 2017
Excellence in Leadership Award, UC Davis Health System, 2015-2016
Faculty Teaching Award, Department of Pathology & Laboratory medicine UC Davis School of Medicine, 2014-2015
UCDMC Quality Project Award with Dr. Datta-Mitra (Lead Faculty), 2015
UCSD Medical School Kaiser Teaching Award for 1st year medical students, 2011
Clinical Pathology Teacher of the Year, UCSD School of Medicine, 2009-2010
Administrative Chief Resident, Dept. of Pathology, Yale School of Medicine, 2007-2008
Administrative Chief Resident, Dept. of Lab Med, Yale School of Medicine, 2007-2008
CAP Resident Delegate, Yale School of Medicine, 2007-2008
Harvard Medical School, Teaching Award Nominee as resident, 2003-2004
Magna Cum Laude, UCSD, 1997
Phi Beta Kappa, UCSD, 1997
Departmental Distinction, Dept. of Biology, UCSD, July 1997
Honors Thesis in Biochemistry, UCSD, 1997
Teaching Award for T.A. Excellence, UCSD, 1996-1997 Academic Year
Representative Publications
  1. Andrew D Jones, MD, John Paul Graff, DO, MD, Morgan Darrow, MD, Alexander Borowsky, MD, Kristin A. Olson, MD, Kristin A. Olson, MD, Regina Gandour-Edwards, MD, Ananya Datta-Mitra, MD, Dongguang Wei, MD, PhD, Guofeng Gao, MD PhD, Blythe Durbin-Johnson, PhD, and Hooman H. Rashidi*, MD. Impact of pre-analytic variables on deep learning accuracy in histopathology. Histopathology 2019
  2. Nam K. Tran, PhD, HCLD (ABB)1*, FACB; Soman Sen, MD, FACS2; Tina L. Palmieri, MD, FACS, FCCM2; Kelly Lima, BS1; Stephanie Falwell, BS1; Jeffrey Wajda, DO3; and Hooman H. Rashidi, MD, FASCP1*. Artificial Intelligence and Machine Learning for Predicting Acute Kidney Injury in Severely Burned Patients: A Proof of Concept. Burns 2019
  3. Hooman Rashidi, MD, Nam K. Tran, PhD, Elham Vali Betts, MD, Lydia P. Howell, MD, Ralph Green, MD, PhD. Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods. Academic Pathology, September 2019.
  4. Hooman H. Rashidi, MD, FASCP1*; Soman Sen, MD, FACS2; Tina L. Palmieri, MD, FACS, FCCM2; Thomas Blackmon, BS1; Jeffrey Wajda, DO3; and Nam K. Tran, PhD, HCLD (ABB), FACB1*. Early Recognition of Burn- and Trauma-Related Acute Kidney Injury: A Pilot Comparison of Machine Learning Techniques. Scientific Reports. Jan 2020
  5. Lorne Holland, MD, Dongguang Wei, MD, PhD, Kristin A. Olson, MD, John Paul Graff, DO, Andrew D Jones, MD, Blythe Durbin-Johnson, PhD, Ananya Datta-Mitra, MD, and Hooman H. Rashidi, MD. Effect of training set size on deep learning classification accuracy, a proof of concept of machine learning models for colon histology. Journal of Pathology Informatics. Feb 2020
  6. Nam K. Tran, PhD, Samer Albahra, MD, Tam N. Pham, MD, James Holmes IV, MD, David Greenhalgh, MD, Tina L. Palmieri, MD, Jeffrey Wajda, and Hooman H. Rashidi, MD. Novel Application of An Automated-Machine Learning Development Tool for Predicting Burn Sepsis. (July 2020; Scientific Reports)
  7. Hooman H. Rashidi, MD, FASCP1*, Amy Makley, MD2; Tina L. Palmieri, MD, FACS, FCCM3; Samer Albahra, MD1; Julia Loegering, BS1; Lei Fang, PhD4; Kensuke Yamaguchi, PhD4; Travis Gerlach, MD5; Dario Rodriquez Jr, MSc, RRT, FAARC6; and Nam K. Tran, PhD, HCLD (ABB), FAACC. Enhancing Military Burn- and Trauma-Related Acute Kidney Injury Prediction through an Automated Machine Learning Platform and Point-of-Care Testing. (Sept 2020; Archives of Pathology & Laboratory Medicine)
  8. Elham Vali-Betts, Kevin J. Krause, Alanna Dubrovsky, Kristin Olson, John Paul Graff, Anupam Mitra, Ananya Datta-Mitra, Kenneth Beck, Aristotelis Tsirigos, Cynthia Loomis, Antonio Galvao Neto, Esther Adler, Hooman H Rashidi. Effects of Image Quantity and Image Source Variation on Machine Learning Histology Differential Diagnosis Models. Journal of Pathology Informatics. Jan 2021.
  9. Kuang-Yu Jen, Samer Albahra, Felicia Yen, Junichiro Sageshima, Ling-Xin Chen, Hooman H. Rashidi.  Automated En Masse Machine Learning Model Generation Shows Comparable Performance as Classic Regression Models for Predicting Delayed Graft Function in Renal Allografts. (Jan 2021; Transplantation) 
  10. Nam K. Tran, Taylor Howard, Ryan Walsh, John Pepper, Julia Loegering, and Hooman H. Rashidi. NOVEL APPLICATION OF MACHINE LEARNING WITH MALDI-TOF-MS FOR RAPID HIGH-THROUGHPUT IDENTIFICATION OF COVID-19: A PROOF OF CONCEPT. Scientific Reports. March 2021
  11. Hooman H. Rashidi, Nam Tran PhD, Samer Albahra MD,  Luke T. Dang. Machine Learning in Healthcare and Laboratory Medicine: General Overview of Supervised Learning and Auto-ML. Int. J. Lab. Hematology. July 2021.
  12. Rashidi HH, Luke T. Dang, Samer Albahra, Resmi Ravindran & Imran Khan. Automated Machine Learning for Endemic Active Tuberculosis Prediction from Multiplex Serological Data: Proof of Concept. Nature’s Scientfic Reports. Sept. 2021. 
  13. Tran NK, Samer Albahra, Larissa May, Sarah Waldman, Scott Crabtree, Scott Bainbridge, Rashidi HH. Evolving Applications of Artificial Intelligence and Machine Learning in Infectious Diseases Testing. Clin. Chem. Jan 2022. 
  14. Hooman H. Rashidi, Imran Khan, Ujjwal Ratan, Nihir, Chadderwala, Wilson To, Prathima Srinivas, Jeffery Wajda, Luke T. Dang, Nam K. Tran. Prediction of Tuberculosis using an Automated Machine Learning platform for Models Trained on Synthetic Data: A Proof of Concept. J. Path. Info. Jan 2022. 
  15. Hooman H Rashidi. John Pepper, Taylor Howard, Karina Klein, Larissa May, Samer Albahra, Bret Phinney, Michelle Salemi, Nam K Tran. Comparative performance of two automated machine learning platforms for Covid-19 detection by MALDI-TOF-MS.  PLOS One, 2022.
  16. Hooman H Rashidi, Kelly Bowers, Morayma Gil Reyes. Machine learning in the coagulation and hemostasis arena: An overview and evaluation of methods, review of literature, and future directions. J. Thromb. Hemostasis 2023.
  17. Samer Albahra, Tom Gorbett, Scott Robertson, Giana D’Aleo, Sushasree Vasudevan Suseel Kumar, Samuel Ockunzzi, Daniel Lallo, Bo Hu, Hooman H. Rashidi. Artificial intelligence and machine learning overview in pathology & laboratory medicine: A general review of data preprocessing and basic supervised concepts. Seminars in Diagnostic Pathology. Volume 40, 71-87, 2023.
  18. Hooman H. Rashidi, Samer Albahra, Scott Robertson, Nam K Tran, Bo Hu. Common statistical concepts in the supervised Machine Learning arena. Front. Oncol. 13:1130229. 2023.
  19. Rashidi HH, Fennell BD, Albahra S, Hu B, Gorbett T. The ChatGPT conundrum: Human-generated scientific manuscripts misidentified as AI creations by AI text detection tool, J Path Inform, Oct. 2023. https://doi.org/10.1016/j.jpi.2023.100342
  20. Pantanowitz J, Manko CD, Pantanowitz L. Rashidi HH. Synthetic Data and its Utility in Pathology and Laboratory Medicine. Lab Invest. 2024 Jun 24:102095. doi: 10.1016/j.labinv.2024.102095.
  21. Rashidi HH, Ikram A, Dang LT, Bashir A, Zohra T, Ali A, Tanvir H, Mudassar M, Ravindran R, Akhtar N, Sikandar RI, Umer M, Akhter N, Butt R, Fennell BD, Khan IH. Comparing machine learning screening approaches using clinical data and cytokine profiles for COVID-19 in resource-limited and resource-abundant settings. Sci Rep. 2024 Jun 28;14(1):14892. doi: 10.1038/s41598-024-63707-3.
  22. Wu CP, Sleiman J, Fakhry B, Chedraoui C, Attaway A, Bhattacharyya A, Bleecker ER, Erdemir A, Hu B, Kethireddy S, Meyers DA, Rashidi HH, Zein JG. Novel Machine Learning Identifies 5 Asthma Phenotypes Using Cluster Analysis of Real-World Data. J Allergy Clin Immunol Pract. 2024 Aug;12(8):2084-2091.e4. doi: 10.1016/j.jaip.2024.04.035. Epub 2024 Apr 27. PMID: 38685479; PMCID: PMC11340628.
  23. Reyes Gil M, Pantanowitz J, Rashidi HH. Venous thromboembolism in the era of machine learning and artificial intelligence in medicine. Thromb Res. 2024 Oct;242:109121. doi: 10.1016/j.thromres.2024.109121. Epub 2024 Aug 23. PMID: 39213896.
  24. Pantanowitz L, Hanna M, Pantanowitz J, Lennerz J, Henricks WH, Shen P, Quinn B, Bennet S, Rashidi HH. Regulatory Aspects of Artificial Intelligence and Machine Learning. Mod Pathol. 2024 Dec;37(12):100609. doi: 10.1016/j.modpat.2024.100609. Epub 2024 Sep 12. PMID: 39260776.
  25. Rashidi HH, Albahra S, Rubin BP, Hu B. A novel and fully automated platform for synthetic tabular data generation and validation. Sci Rep. 2024 Oct 7;14(1):23312. doi: 10.1038/s41598-024-73608-0. PMID: 39375404; PMCID: PMC11458594.
  26. Littlefield N, Amirian S, Biehl J, Andrews EG, Kann M, Myers N, Reid L, Yates AJ Jr, McGrory BJ, Parmanto B, Seyler TM, Plate JF, Rashidi HH, Tafti AP. Generative AI in orthopedics: an explainable deep few-shot image augmentation pipeline for plain knee radiographs and Kellgren-Lawrence grading. J Am Med Inform Assoc. 2024 Nov 1;31(11):2668-2678. doi: 10.1093/jamia/ocae246. PMID: 39311859; PMCID: PMC11491597.
  27. Rashidi HH, Hu B, Pantanowitz J, Tran N, Liu S, Chamanzar A, Gur M, Chang CH, Wang Y, Tafti A, Pantanowitz L, Hanna MG. Statistics of Generative Artificial Intelligence and Nongenerative Predictive Analytics Machine Learning in Medicine. Mod Pathol. 2025 Mar;38(3):100663. doi: 10.1016/j.modpat.2024.100663. Epub 2024 Nov 22. PMID: 39579984.
  28. Pantanowitz L, Pearce T, Abukhiran I, Hanna M, Wheeler S, Soong TR, Tafti AP, Pantanowitz J, Lu MY, Mahmood F, Gu Q, Rashidi HH. Nongenerative Artificial Intelligence in Medicine: Advancements and Applications in Supervised and Unsupervised Machine Learning. Mod Pathol. 2025 Mar;38(3):100680. doi: 10.1016/j.modpat.2024.100680. Epub 2024 Dec 13. PMID: 39675426.
  29. Hanna MG, Pantanowitz L, Jackson B, Palmer O, Visweswaran S, Pantanowitz J, Deebajah M, Rashidi HH. Ethical and Bias Considerations in Artificial Intelligence/Machine Learning. Mod Pathol. 2025 Mar;38(3):100686. doi: 10.1016/j.modpat.2024.100686. Epub 2024 Dec 16. PMID: 39694331.
  30. Rashidi HH, Pantanowitz J, Hanna MG, Tafti AP, Sanghani P, Buchinsky A, Fennell B, Deebajah M, Wheeler S, Pearce T, Abukhiran I, Robertson S, Palmer O, Gur M, Tran NK, Pantanowitz L. Introduction to Artificial Intelligence and Machine Learning in Pathology and Medicine: Generative and Nongenerative Artificial Intelligence Basics. Mod Pathol. 2025 Apr;38(4):100688. doi: 10.1016/j.modpat.2024.100688. Epub 2025 Jan 3. PMID: 39755237.
  31. Hanna MG, Pantanowitz L, Dash R, Harrison JH, Deebajah M, Pantanowitz J, Rashidi HH. Future of Artificial Intelligence-Machine Learning Trends in Pathology and Medicine. Mod Pathol. 2025 Apr;38(4):100705. doi: 10.1016/j.modpat.2025.100705. Epub 2025 Jan 4. PMID: 39761872.
  32. Jackson BR, Rashidi HH, Lennerz JK, de Baca ME. Ethical and Regulatory Perspectives on Generative Artificial Intelligence in Pathology. Arch Pathol Lab Med. 2025 Feb 1;149(2):123-129. doi: 10.5858/arpa.2024-0205-RA. PMID: 39299704.
  33. McCaffrey P, Jackups R, Seheult J, Zaydman MA, Balis U, Thaker HM, Rashidi H, Gullapalli RR. Evaluating Use of Generative Artificial Intelligence in Clinical Pathology Practice: Opportunities and the Way Forward. Arch Pathol Lab Med. 2025 Feb 1;149(2):130-141. doi: 10.5858/arpa.2024-0208-RA. PMID: 39384182.
  34. Kamble TS, Wang H, Myers N, Littlefield N, Reid L, McCarthy CS, Lee YJ, Liu H, Pantanowitz L, Amirian S, Rashidi HH, Tafti AP. Predicting cancer survival at different stages: Insights from fair and explainable machine learning approaches. Int J Med Inform. 2025 May;197:105822. doi: 10.1016/j.ijmedinf.2025.105822. Epub 2025 Feb 13. PMID: 39970491.
  35. Rashidi HH, Hanna MG, Pantanowitz L. Introducing an Essential 7-Part Artificial Intelligence Review Series: A Guided Journey Into the Future of Pathology and Medicine. Mod Pathol. 2025 Mar;38(3):100673. doi: 10.1016/j.modpat.2024.100673. Epub 2024 Nov 26. PMID: 39603531.
  36. Pantanowitz L, Pearce T, Abukhiran I, Hanna M, Wheeler S, Soong TR, Tafti AP, Pantanowitz J, Lu MY, Mahmood F, Gu Q, Rashidi HH. Nongenerative Artificial Intelligence in Medicine: Advancements and Applications in Supervised and Unsupervised Machine Learning. Mod Pathol. 2025 Mar;38(3):100680. doi: 10.1016/j.modpat.2024.100680. Epub 2024 Dec 13. PMID: 39675426.
  37. S. Amirian et al., "State-of-the-Art in Responsible, Explainable, and Fair AI for Medical Image Analysis," in IEEE Access, vol. 13, pp. 58229-58263, 2025, doi: 10.1109/ACCESS.2025.3555543.
  38. Rashidi HH, Pantanowitz J, Chamanzar A, Fennell B, Wang Y, Gullapalli RR, Tafti A, Deebajah M, Albahra S, Glassy E, Hanna MG, Pantanowitz L. Generative Artificial Intelligence in Pathology and Medicine: A Deeper Dive. Mod Pathol. 2025 Apr;38(4):100687. doi: 10.1016/j.modpat.2024.100687. Epub 2024 Dec 15. PMID: 39689760.
  39. Rashidi HH, Pantanowitz J, Hanna MG, Tafti AP, Sanghani P, Buchinsky A, Fennell B, Deebajah M, Wheeler S, Pearce T, Abukhiran I, Robertson S, Palmer O, Gur M, Tran NK, Pantanowitz L. Introduction to Artificial Intelligence and Machine Learning in Pathology and Medicine: Generative and Nongenerative Artificial Intelligence Basics. Mod Pathol. 2025 Apr;38(4):100688. doi: 10.1016/j.modpat.2024.100688. Epub 2025 Jan 3. PMID: 39755237.
  40. Robertson S, Koppireddy V, Cumbo J, Rashidi H, Albahra S. PIRO: A web-based search platform for pathology reports, leveraging large language models to generate discrete searchable insights. J Pathol Inform. 2025 Mar 18;17:100436. doi: 10.1016/j.jpi.2025.100436. PMID: 40236563; PMCID: PMC11999576.