Artificial intelligence for diagnosing rare bone diseases: a global survey of healthcare professionals
Most survey participants are open to integrating image recognition AI into their RBD diagnostic workflow. However, concerns about AI-related errors, privacy, and model interpretability highlight the importance of transparent collaboration between developers and healthcare professionals throughout the development process to ensure that such technologies are clinically trustworthy and practically adoptable.
Authors
- Behnam Javanmardi (ORCID: https://orcid.org/0000-0002-9317-6114)
- Rebekah L. Waikel (ORCID: https://orcid.org/0000-0001-8635-7542)
- Peter Krawitz (ORCID: https://orcid.org/0009-0008-1789-3200)
- Shahida Moosa (ORCID: https://orcid.org/0000-0002-4463-3067)
- Klaus L. Mohnike (ORCID: https://orcid.org/0000-0001-5113-8657)
- Tinatin Tkemaladze (ORCID: https://orcid.org/0000-0003-1924-6695)
- Jean Tori Pantel (ORCID: https://orcid.org/0000-0002-2674-4660)
- Benjamin D. Solomon (ORCID: https://orcid.org/0000-0002-8826-1023)
- Alexander Küsshauer
- Minu Fardipour
Institutions
- University of Bonn (DE)
- Stellenbosch University (ZA)
- University Hospital Bonn (DE)
- National Human Genome Research Institute (US)
- University Hospital Magdeburg (DE)
- Tbilisi State Medical University (GE)
- RWTH Aachen University (DE)
Publication Details
- Journal
- Orphanet Journal of Rare Diseases
- Published
- 2025-07-16
- DOI
- https://doi.org/10.1186/s13023-025-03875-1
- Primary Topic
- Cancer Genomics and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Bundesministerium für Bildung und Forschung
- National Institutes of Health
- National Human Genome Research Institute