Artificial intelligence for diagnosing rare bone diseases: a global survey of healthcare professionals
Abstract Background Rare bone diseases (RBDs) are an important group of conditions characterized by abnormalities in bone and cartilage. Their large number, individual rarity, and heterogeneity make accurate and timely diagnosis challenging. Establishing correlations between genotype and phenotype (...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
|
| Series: | Orphanet Journal of Rare Diseases |
| Online Access: | https://doi.org/10.1186/s13023-025-03875-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849235631006285824 |
|---|---|
| author | Behnam Javanmardi Rebekah L. Waikel Tinatin Tkemaladze Shahida Moosa Alexander Küsshauer Jean Tori Pantel Minu Fardipour Peter Krawitz Benjamin D. Solomon Klaus Mohnike |
| author_facet | Behnam Javanmardi Rebekah L. Waikel Tinatin Tkemaladze Shahida Moosa Alexander Küsshauer Jean Tori Pantel Minu Fardipour Peter Krawitz Benjamin D. Solomon Klaus Mohnike |
| author_sort | Behnam Javanmardi |
| collection | DOAJ |
| description | Abstract Background Rare bone diseases (RBDs) are an important group of conditions characterized by abnormalities in bone and cartilage. Their large number, individual rarity, and heterogeneity make accurate and timely diagnosis challenging. Establishing correlations between genotype and phenotype (mainly via imaging) is critical for diagnosing RBDs. Image recognition artificial intelligence (AI) has the potential to significantly improve the diagnostic process by assisting healthcare providers to identify and differentiate imaging patterns associated with various RBDs. This survey study sought to assess the interest of various healthcare providers worldwide in utilizing an AI-based assistant tool for the differential diagnosis of RBDs. Method Survey data were collected from March to September 2024. The survey was performed online and the link was disseminated via direct email, newsletters, and flyers at scientific talks and conferences. Results We received 103 completed surveys, representing respondents from 27 different countries covering most global regions, but mostly from Europe, the United States, and Canada. The majority of the participants are physicians (n = 92, 89%) and primarily work at academic medical centers (n = 84, 81%). While each participant could select multiple specialties, the most frequent clinician types were medical geneticists, pediatricians, and endocrinologists, accounting for 71 (69%) of the respondents. Ninety-four (91%) of the respondents find imaging to be very or extremely important, and the majority (n = 84, 81%) consider X-rays to be the most important imaging modality. Although around half of the participants (n = 45) have concerns about AI-related errors and consider the explainability of AI algorithms to be very (42/103) or extremely (9/103) important, 81% of the respondents report that they are somewhat (n = 39) or extremely (n = 45) likely to consider integrating image recognition AI into their current diagnostic workflow. Conclusions 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. |
| format | Article |
| id | doaj-art-9ae64951d24f4784a52d7f966ed04e8f |
| institution | Kabale University |
| issn | 1750-1172 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Orphanet Journal of Rare Diseases |
| spelling | doaj-art-9ae64951d24f4784a52d7f966ed04e8f2025-08-20T04:02:44ZengBMCOrphanet Journal of Rare Diseases1750-11722025-07-0120111010.1186/s13023-025-03875-1Artificial intelligence for diagnosing rare bone diseases: a global survey of healthcare professionalsBehnam Javanmardi0Rebekah L. Waikel1Tinatin Tkemaladze2Shahida Moosa3Alexander Küsshauer4Jean Tori Pantel5Minu Fardipour6Peter Krawitz7Benjamin D. Solomon8Klaus Mohnike9Institute for Genomic Statistics and Bioinformatics, University Hospital BonnNational Human Genome Research InstituteDepartment of Molecular and Medical Genetics, Tbilisi State Medical UniversityDivision of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch UniversityTransfer Center enaCom, University of BonnInstitute for Digitalization and General Medicine, Medical Faculty, RWTH Aachen UniversityUniversity Hospital MagdeburgInstitute for Genomic Statistics and Bioinformatics, University Hospital BonnNational Human Genome Research InstituteUniversity Hospital MagdeburgAbstract Background Rare bone diseases (RBDs) are an important group of conditions characterized by abnormalities in bone and cartilage. Their large number, individual rarity, and heterogeneity make accurate and timely diagnosis challenging. Establishing correlations between genotype and phenotype (mainly via imaging) is critical for diagnosing RBDs. Image recognition artificial intelligence (AI) has the potential to significantly improve the diagnostic process by assisting healthcare providers to identify and differentiate imaging patterns associated with various RBDs. This survey study sought to assess the interest of various healthcare providers worldwide in utilizing an AI-based assistant tool for the differential diagnosis of RBDs. Method Survey data were collected from March to September 2024. The survey was performed online and the link was disseminated via direct email, newsletters, and flyers at scientific talks and conferences. Results We received 103 completed surveys, representing respondents from 27 different countries covering most global regions, but mostly from Europe, the United States, and Canada. The majority of the participants are physicians (n = 92, 89%) and primarily work at academic medical centers (n = 84, 81%). While each participant could select multiple specialties, the most frequent clinician types were medical geneticists, pediatricians, and endocrinologists, accounting for 71 (69%) of the respondents. Ninety-four (91%) of the respondents find imaging to be very or extremely important, and the majority (n = 84, 81%) consider X-rays to be the most important imaging modality. Although around half of the participants (n = 45) have concerns about AI-related errors and consider the explainability of AI algorithms to be very (42/103) or extremely (9/103) important, 81% of the respondents report that they are somewhat (n = 39) or extremely (n = 45) likely to consider integrating image recognition AI into their current diagnostic workflow. Conclusions 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.https://doi.org/10.1186/s13023-025-03875-1 |
| spellingShingle | Behnam Javanmardi Rebekah L. Waikel Tinatin Tkemaladze Shahida Moosa Alexander Küsshauer Jean Tori Pantel Minu Fardipour Peter Krawitz Benjamin D. Solomon Klaus Mohnike Artificial intelligence for diagnosing rare bone diseases: a global survey of healthcare professionals Orphanet Journal of Rare Diseases |
| title | Artificial intelligence for diagnosing rare bone diseases: a global survey of healthcare professionals |
| title_full | Artificial intelligence for diagnosing rare bone diseases: a global survey of healthcare professionals |
| title_fullStr | Artificial intelligence for diagnosing rare bone diseases: a global survey of healthcare professionals |
| title_full_unstemmed | Artificial intelligence for diagnosing rare bone diseases: a global survey of healthcare professionals |
| title_short | Artificial intelligence for diagnosing rare bone diseases: a global survey of healthcare professionals |
| title_sort | artificial intelligence for diagnosing rare bone diseases a global survey of healthcare professionals |
| url | https://doi.org/10.1186/s13023-025-03875-1 |
| work_keys_str_mv | AT behnamjavanmardi artificialintelligencefordiagnosingrarebonediseasesaglobalsurveyofhealthcareprofessionals AT rebekahlwaikel artificialintelligencefordiagnosingrarebonediseasesaglobalsurveyofhealthcareprofessionals AT tinatintkemaladze artificialintelligencefordiagnosingrarebonediseasesaglobalsurveyofhealthcareprofessionals AT shahidamoosa artificialintelligencefordiagnosingrarebonediseasesaglobalsurveyofhealthcareprofessionals AT alexanderkusshauer artificialintelligencefordiagnosingrarebonediseasesaglobalsurveyofhealthcareprofessionals AT jeantoripantel artificialintelligencefordiagnosingrarebonediseasesaglobalsurveyofhealthcareprofessionals AT minufardipour artificialintelligencefordiagnosingrarebonediseasesaglobalsurveyofhealthcareprofessionals AT peterkrawitz artificialintelligencefordiagnosingrarebonediseasesaglobalsurveyofhealthcareprofessionals AT benjamindsolomon artificialintelligencefordiagnosingrarebonediseasesaglobalsurveyofhealthcareprofessionals AT klausmohnike artificialintelligencefordiagnosingrarebonediseasesaglobalsurveyofhealthcareprofessionals |