Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review

Barbara Kenner, Suresh T. Chari, David Kelsen, David S. Klimstra, Stephen J. Pandol, Michael Rosenthal, Anil K. Rustgi, James A. Taylor, Adam Yala, Noura Abul-Husn, Dana K. Andersen, David Bernstein, Søren Brunak, Marcia Irene Canto, Yonina C. Eldar, Elliot K. Fishman, Julie Fleshman, Vay Liang W. Go, Jane M. Holt, Bruce FieldAnn Goldberg, William Hoos, Christine Iacobuzio-Donahue, Debiao Li, Graham Lidgard, Anirban Maitra, Lynn M. Matrisian, Sung Poblete, Laura Rothschild, Chris Sander, Lawrence H. Schwartz, Uri Shalit, Sudhir Srivastava, Brian Wolpin

Research output: Contribution to journalArticlepeer-review

62 Scopus citations


Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit ( in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer - Current Efforts; Collaborative Opportunities; and Moving Forward - Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders.

Original languageEnglish
Pages (from-to)251-279
Number of pages29
Issue number3
StatePublished - 1 Mar 2021


  • artificial intelligence
  • early detection
  • machine learning
  • pancreatic cancer


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