Explainable Artificial Intelligence in Paediatric: Challenges for the Future
Paediatrics is a very sensitive domain where consequences of misinterpreting AI outcomes might be very significant. XAI should be adopted carefully with focus on evaluating the outcomes primarily by including paediatricians in the loop, enriching the pipeline by injecting domain knowledge promoting a cross-fertilization perspective aiming at filling the gaps still preventing its adoption.
Authors
- Ahmed Salih (ORCID: https://orcid.org/0000-0002-0871-8282)
- Gloria Menegaz (ORCID: https://orcid.org/0000-0002-6889-3461)
- Elaine M. Boyle (ORCID: https://orcid.org/0000-0002-5038-3148)
- Thillagavathie Pillay (ORCID: https://orcid.org/0000-0002-4159-3282)
Institutions
- University of Verona (IT)
- University of Wolverhampton (GB)
- St Bartholomew's Hospital (GB)
- University of Leicester (GB)
- Queen Mary University of London (GB)
- Barts Health NHS Trust (GB)
- University of Zakho (IQ)
- William Harvey Research Institute (GB)
Publication Details
- Journal
- Health Science Reports
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1002/hsr2.70271
- Citations
- 5
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.60