Performance of explainable artificial intelligence in guiding the management of patients with a pancreatic cyst
EBM models had greater sensitivity and specificity for identifying the correct management compared with either clinical management or previous AI models. The model predictions are demonstrated to be interpretable by clinicians.
Authors
- Ralph H. Hruban (ORCID: https://orcid.org/0000-0003-4554-5672)
- Elliot K. Fishman (ORCID: https://orcid.org/0000-0002-2567-1658)
- Elham Afghani (ORCID: https://orcid.org/0000-0002-9014-9735)
- Rahul Dodhia (ORCID: https://orcid.org/0000-0003-3812-3906)
- Anne Marie Lennon (ORCID: https://orcid.org/0000-0001-8957-3945)
- Felipe Oviedo (ORCID: https://orcid.org/0000-0001-9480-6539)
- Juan Lavista Ferres (ORCID: https://orcid.org/0000-0002-9654-3178)
- William B. Weeks (ORCID: https://orcid.org/0000-0002-9918-1360)
- Alison Patricia Klein (ORCID: https://orcid.org/0000-0003-2737-8399)
- Caleb Robinson
- Linda Chi Hang Chu (ORCID: https://orcid.org/0000-0001-9729-2756)
- Jin Peng He (ORCID: https://orcid.org/0000-0001-9149-2247)
- Ammar A. Javed (ORCID: https://orcid.org/0000-0002-5463-5250)
- Satomi Kawamoto (ORCID: https://orcid.org/0000-0002-3577-1388)
- Christopher L. Wolfgang (ORCID: https://orcid.org/0000-0002-8717-0373)
- Nick Papadopolous
- Mike Goggins
- Ken W Kinzler
Institutions
- Microsoft (United States) (US)
- Johns Hopkins University (US)
- Johns Hopkins Medicine (US)
- Cancer Research Center (US)
- Sidney Kimmel Comprehensive Cancer Center (US)
- New York University (US)
Publication Details
- Journal
- Pancreatology
- Published
- 2024-09-03
- DOI
- https://doi.org/10.1016/j.pan.2024.09.001
- Citations
- 5
- Primary Topic
- Pancreatic and Hepatic Oncology Research
- Type
- article
- Field-Weighted Citation Impact
- 1.41
Funders
- Microsoft
- Lustgarten Foundation
- National Institutes of Health