The Medical Sieve Radiology Grand Challenge on Chest X-rays
The Medical Sieve Radiology Grand Challenge on Chest X-rays


With the promise of AI, the task of automating preliminary read reports for common exams such as chest X-rays to expedite clinical workflows and improve operational efficiencies in hospitals appear likely. The Medical Sieve Grand Challenge was an ambitious attempt to reach this capability which took several years of concerted and collaborative effort between radiologists, clinicians, AI researchers and software engineers. In this talk I give an overview of the large-scale data science effort initiated by IBM Research to produce such a fully automated preliminary read capability covering a comprehensive list of findings that match or exceed the performance of entry-level radiologists. The work involved making simultaneous advancements in many fields of AI/Data Science ranging from design of neural networks, text analytics, to building clinical knowledge and reasoning systems and conducting various clinical studies to qualitatively and quantitatively assess the performance of AI systems in healthcare.


Dr. Tanveer Syeda-Mahmood is an IBM Fellow and Chief Scientist/overall lead for the Medical Sieve Radiology Grand Challenge project in IBM Research, Almaden. Medical Sieve is an exploratory research project with global participation from many IBM Research Labs around the world including Almaden Labs in San Jose, CA, Haifa Research Labs in Israel and Melbourne Research Lab in Australia. The goal of this project is to develop automated radiology and cardiology assistants of the future that help clinicians in their decision making.

Dr. Syeda-Mahmood graduated from the MIT AI Lab in 1993 with a Ph.D in Computer Science. Prior to IBM, she worked as a Research Staff Member at Xerox Webster Research Center, Webster, NY. She joined IBM Almaden Research Center in 1998.

Prior to coming to IBM, Dr. Syeda-Mahmood led the image indexing program at Xerox Research and was one of the early originators of the field of content-based image and video retrieval. Currently, she is working on applications of content-based retrieval in healthcare and medical imaging. Over the past 30 years, her research interests have been in a variety of areas relating to artificial intelligence including computer vision, image and video databases, medical image analysis, bioinformatics, signal processing, document analysis, and distributed computing frameworks. She has over 200 refereed publications and over 80 patent filed.

Dr. Syeda-Mahmood will be the General Chair of MICCAI 2023, the premier conference in medical imaging. She was the General Chair of the First IEEE International Conference on Healthcare Informatics, Imaging,and Systems Biology, San Jose, CA 2011. She was also the program co-chair of CVPR 2008.

Dr. Syeda-Mahmood is a Fellow of IEEE. She is also the first IBMer to become an AIMBE Fellow. She is also a member of IBM Academy of Technology. Dr. Syeda-Mahmood was declared Master Inventor in 2011 and in 2019. She is the recipient of key awards including IBM Corporate Award 2015, Best of IBM Award 2015, 2016 and several outstanding innovation awards.

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