Peter Szolovits
Professor of Computer Science and Engineering (Post-Tenure)
Applies AI methods to medical decision making, including natural language processing to extract data from clinical narratives.
Peter Szolovits's lab at MIT works on medical imaging & healthcare AI and reinforcement learning. 19 papers since 2023, 2 at top venues, mostly JAMIA and Nature Machine Intelligence. 1 PhD student. 216 alumni. Applications close .
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 11%
- PhD students
- 1
- MS students
- 0
- alumni listed
- 216
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 6 papers2025 · T1 0
- 6 papers2024 · T1 1
- 7 papers2023 · T1 1
T1 venuesOtherVenues: JAMIA, Nature Machine Intelligence
Research focuspaper titles that use each topic's words
- Medical imaging & healthcare AI5 papers
- Reinforcement learning3 papers
- clinical3 papers
- making2 papers
- decision making0 papers
Paper list
From the lab homepage.
20256 papers
- Computers, 14(6)Ensemble-Based Biometric Verification: Defending Against Multi-Strategy Deepfake Image Generation (opens in a new tab)
- MAKE, MDPIEvaluating Prompt Injection Attacks with LSTM-Based Generative Adversarial Networks: A Lightweight Alternative to Large Language Models (opens in a new tab)
- Applied SciencesFairRAG: A Privacy-Preserving Framework for Fair Financial Decision-Making (opens in a new tab)
- Telehealth and Medicine TodayIntegrating Large Language Models into Clinical Decision Support Systems: A Novel Approach to UTI Diagnosis and Treatment (opens in a new tab)
- AI and Ethics, 5(2), 1743--1756Optimizing Fairness and Accuracy: A Pareto Optimal Approach for Decision-Making (opens in a new tab)
- International Journal of Medical Informatics, Elsevier BV, 106015Toward Responsible AI Governance: Balancing Multi-Stakeholder Perspectives on AI in Healthcare (opens in a new tab)
20246 papers
- Machine Learning and Knowledge Extraction, 6(3), 2130-48A Multi-Objective Framework for Balancing Fairness and Accuracy in Debiasing Machine Learning Models (opens in a new tab)
- Lancet Digital Health, 6(1), E12--E22Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study (opens in a new tab)
- AI Doctor: The Rise of Artificial Intelligence in Healthcare - A Guide for Users, Buyers, Builders, and Investors, WileyForeword
- NEJM-AILarge Language Models Seem Miraculous, but Science Abhors Miracles (opens in a new tab)
- United European Gastroenterology ConferenceTesting machine learning with Finnish register data to predict non-response to conventional treatment in patients with inflammatory bowel disease [poster]
- JAMIA (T1)Using Machine Learning to Develop Smart Reflex Testing Protocols (opens in a new tab)
20237 papers
- J Am Med Inform AssocAn Open Natural Language Processing (NLP) Framework for EHR-based Clinical Research: A Case Demonstration Using the National COVID Cohort Collaborative (N3C) (opens in a new tab)
- Clinics in Laboratory Medicine, 43(1), 29--46Clinical Artificial Intelligence: Design Principles and Fallacies (opens in a new tab)
- JUCS - Journal of Universal Computer Science, 29(1), 3-15EntailClass: A Classification Approach to EntailSum and End-to-End Document Extraction, Identification, and Evaluation (opens in a new tab)
- Telehealth and Medicine TodayPragmatic Approaches to Interoperability – Surmounting Barriers to Healthcare Data and Information Across Organizations and Political Boundaries (opens in a new tab)
- Nature Machine Intelligence (T1)Structure Inducing Pre-Training (opens in a new tab)
- Telehealth and Medicine TodaySurmounting Barriers to Healthcare Data and Information: Cases in Point, the U.S. Experience (opens in a new tab)
- Telehealth and Medicine TodaySurmounting Barriers to Healthcare Data and Information: International Case Studies (opens in a new tab)
Where alumni went216 listed
Apply to MITFall 2027
MIT EECS Computer Science PhD
- Round
- Single round
- Opens
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- GRE
- See program page
- English
- TOEFL/IELTS
- Fee
- USD 90
Confirmed on the program page.
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