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MIT Computer Science and Artificial Intelligence Lab black bag

Clinical Decision Making

Group Information

Director: Peter Szolovits
Mission: To provide better health care through applied artificial intelligence.
Description: The Clinical Decision Making Group at the MIT Laboratory for Computer Science is a research group dedicated to exploring and furthering the application of technology and artificial intelligence to clinical situations. Because of the vital and crucial nature of medical practice, and the need for accurate and timely information to support clinical decisions, the group is also focused on the gathering, availability, security and use of medical information throughout the human "life cycle" and beyond.


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Phone: (617) 253-5860
Fax: (617) 258-8682
The Stata Center (Bldg. 32)
32 Vassar St #250
Cambridge MA 02139
Click here for detailed directions to our Lab.




i2b2 The i2b2 Center is developing a scalable informatics framework that will bridge clinical research data and the vast data banks arising from basic science research in order to better understand the genetic bases of complex diseases. This knowledge will facilitate the design of targeted therapies for individual patients with diseases having genetic origins.
ICU/MIMIC Develop and evaluate advanced ICU patient monitoring systems that will substantially improve the efficiency, accuracy and timeliness of clinical decision making in intensive care.
Fair Witness A well-organized, complete and accurate record of clinical encounters can form the bedrock of data on which both clinical care and clinical and biomedical research can rest. This project applies state of the art and novel technologies to "listen" to and interpret encounters between patients and health care providers to create such records. Its success should lead to better health care and greater possibilities for using clinical data in medical research.
Medical Text Analysis Developing tools and techniques to "read" unstructured English descriptions of clinical data and knowledge.
NMESH The National Multi- Protocol Ensemble for Self-scaling systems for Health seeks to address problems of scalability and preparedness in health care infrastructure at multiple levels by leveraging off of prior work on multi-institutional "on-the-fly" data integration, regional patient-controlled medical records, self-describing peer-to-peer networks, cryptographic health identification systems, and a GIS-based biosurveillance toolset.
PING/IndivoHealth Indivo is a personally controlled health record system that enables patients to own complete, secure copies of their medical records. Indivo integrates health information across sites of care and over time. Indivo is built to public standards as an open-source application platform and is actively deployed in real-life settings.
Guardian Angel Personal lifelong active medical assistants
Heart Disease Assisting the diagnosis and therapy of cardiovascular disease. Physicians can try the diagnosis program.
Geninfer Assisting clinical genetics counselors
SHARE/ HIID-IT Using cryptographic methods to encourage the protected sharing of data in clinical trials and other privacy-sensitive settings.
MAITA The Monitoring, Analysis, and Interpretation Tool Arsenal provides means for automated gathering, understanding, and reacting to important information in a broad range of application areas, including clinical, military, industrial, commercial, and scientific monitoring and surveillance.
Case-Based Reasoning Learning diagnostic expertise from experience
EMRS The Electronic Medical Record System project demonstrated the feasibility of providing unified common medical record access via the world-wide web.

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