Chatbot assistance in precision oncology treatment decision-making
Artificial intelligence chatbots have shown promise in medical settings, but their ability to interpret complex molecular data is not clear. Here, we assessed 50 different patient scenarios with clinical and molecular data and found that chatbots provided mostly accurate and comprehensive recommendations, although key treatment options were omitted occasionally, and non-data driven treatments were recommended in cases with multiple mutations.
Authors
- Kylie Fletcher (ORCID: https://orcid.org/0000-0003-0723-9236)
- Douglas Buckner Johnson (ORCID: https://orcid.org/0000-0002-6390-773X)
- Marc Matrana (ORCID: https://orcid.org/0000-0002-7301-170X)
- Christine Micheel (ORCID: https://orcid.org/0000-0002-7744-9039)
- Hannah Burnette (ORCID: https://orcid.org/0009-0009-7784-634X)
- Ben Ho Park (ORCID: https://orcid.org/0000-0001-8119-2291)
- Daniel H. Johnson (ORCID: https://orcid.org/0000-0002-4778-686X)
- John T. Cole
- Kevan Simms
Institutions
- Ochsner Health System (US)
- Vanderbilt University (US)
- Ochsner Medical Center (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- The Oncologist
- Published
- 2025-09-27
- DOI
- https://doi.org/10.1093/oncolo/oyaf316
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00