Portrait of Asma Ben Abacha

Asma Ben Abacha

Senior Applied Scientist

About

Dr. Asma Ben Abacha is a Senior Scientist at Microsoft AI working on multimodal AI for healthcare, with a focus on radiology, medical vision-language models, clinical applications, and reliable, clinically aligned evaluation of AI systems. Her research spans multimodal foundation models, medical visual question answering, clinical note generation, and the development of datasets, benchmarks, and evaluation methods for healthcare AI.

She received a Ph.D. in computer science from Paris-Saclay University (opens in new tab), France. Before joining Microsoft, she was a Staff Scientist at the U.S. National Institutes of Health (NIH) (opens in new tab), where her research focused on medical natural language processing, consumer health question answering, and visual question answering, and a researcher at the Luxembourg Institute of Science and Technology (LIST) (opens in new tab), where her research included clinical information retrieval, information extraction, pharmacovigilance, and ontology validation.

She has published more than 90 scientific articles, with 7,400+ citations and an h-index of 40 (DBLP (opens in new tab) | Google Scholar (opens in new tab) | ResearchGate (opens in new tab)). She has served as Senior Area Chair, Area Chair, and reviewer for leading conferences and journals in natural language processing, machine learning, and medical informatics. She is also a co-organizer of the Clinical NLP Workshop (opens in new tab).

She is the founder and lead organizer of the MEDIQA competitions (opens in new tab), a series of international shared tasks in medical AI comprising 12+ competitions and attracting more than 100 participating teams. She has also organized challenges at ImageCLEF and TREC.

Publications:

Academic Service & Leadership:

Invited Talks & Keynotes:
  • From Image Retrieval to Clinical Reasoning: Multimodal Foundation Models, Shared Benchmarks, and Clinically Aligned Evaluation in Radiology and Dermatology. Keynote presented at the Workshop on Large Foundation Models in Biology and Biomedicine, WACV 2026. March 6, 2026.
  • Guest Lecturer on Large Language Models and Applications in Healthcare. University of Illinois Urbana-Champaign, USA. 2024.
  • Medical Computer Vision: Current Limitations of Vision Datasets. Panel on Current Limitations of Vision Datasets at the Future of Computer Vision Datasets Workshop, CVPR 2021. June 20, 2021.
  • AI for Medical NLP and Radiology. Women in STEM Group, University of Essex, UK. March 9, 2021.
  • Guest Lecturer on Multimodal Question Answering in the Medical Domain. Carnegie Mellon University, USA. 2020.
  • Insights from the Organization of International Challenges on Artificial Intelligence in Medical Question Answering. Natural Language Processing and Data Mining for Scientific Text (SciNLP) Workshop. June 24, 2020.
  • NLP Methods for Medical Question Answering. Philips Annual Artificial Intelligence Conference. May 19, 2020.
  • Medical Question Answering: Dealing with the complexity and specificity of consumer health questions and visual questions. Allen Institute for AI (AI2), Seattle, Washington, USA. November 12, 2019.
  • Neural Approaches to Helping Consumers Find Answers to Health-related Information Needs. CHiQA Project. NVIDIA GTC Washington, D.C., USA. November 6, 2019.
  • Methods and Challenges in Consumer Health Question Answering. IBM Research Almaden, CA, USA. March 27, 2019.