Insights from explainable AI in oesophageal cancer team decisions
XAI techniques delineate the relationship between clinical factors and OC treatment decisions. These techniques identify advanced age as heavily influencing decisions based on our model with a greater role in patients with specific tumour characteristics. This study methodology provides the means for exploring conscious/subconscious bias and interrogating inconsistencies in team-based decision-making within the era of AI-driven decision support.
Authors
- Zoë S. Walters (ORCID: https://orcid.org/0000-0002-1835-5868)
- Navamayooran Thavanesan (ORCID: https://orcid.org/0000-0002-7127-9606)
- Charlotte Parfitt (ORCID: https://orcid.org/0000-0002-5486-6331)
- Tim Underwood (ORCID: https://orcid.org/0000-0001-9455-2188)
- Zehor Belkhatir (ORCID: https://orcid.org/0000-0001-7277-3895)
- Elvira Pérez Vallejos (ORCID: https://orcid.org/0000-0002-0258-9440)
- Tayyaba Azim (ORCID: https://orcid.org/0000-0002-6940-7955)
- Sarvapali D. Ramchurn (ORCID: https://orcid.org/0000-0001-9686-4302)
- Arya Farahi (ORCID: https://orcid.org/0000-0003-0777-4618)
- Ganesh Vigneswaran (ORCID: https://orcid.org/0000-0002-4115-428X)
Institutions
- Twitter (United States) (US)
- University of Nottingham (GB)
- University Hospital Southampton NHS Foundation Trust (GB)
- University of Southampton (GB)
- The University of Texas at Austin (US)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2024-08-05
- DOI
- https://doi.org/10.1016/j.compbiomed.2024.108978
- Citations
- 4
- Primary Topic
- Esophageal Cancer Research and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.29
Funders
- University Hospital Southampton NHS Foundation Trust
- UK Research and Innovation
- University of Southampton
- Arts and Humanities Research Council
- Engineering and Physical Sciences Research Council