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Recommendations for diabetic macular edema management by retina specialists and large language model-based artificial intelligence platforms

Authors :
Ayushi Choudhary
Nikhil Gopalakrishnan
Aishwarya Joshi
Divya Balakrishnan
Jay Chhablani
Naresh Kumar Yadav
Nikitha Gurram Reddy
Padmaja Kumari Rani
Priyanka Gandhi
Rohit Shetty
Rupak Roy
Snehal Bavaskar
Vishma Prabhu
Ramesh Venkatesh
Source :
International Journal of Retina and Vitreous, Vol 10, Iss 1, Pp 1-12 (2024)
Publication Year :
2024
Publisher :
BMC, 2024.

Abstract

Abstract Purpose To study the role of artificial intelligence (AI) in developing diabetic macular edema (DME) management recommendations by creating and comparing responses to clinicians in hypothetical AI-generated case scenarios. The study also examined whether its joint recommendations followed national DME management guidelines. Methods The AI hypothetically generated 50 ocular case scenarios from 25 patients using keywords like age, gender, type, duration and control of diabetes, visual acuity, lens status, retinopathy stage, coexisting ocular and systemic co-morbidities, and DME-related retinal imaging findings. For DME and ocular co-morbidity management, we calculated inter-rater agreements (kappa analysis) separately for clinician responses, AI-platforms, and the “majority clinician response” (the maximum number of identical clinician responses) and “majority AI-platform” (the maximum number of identical AI responses). Treatment recommendations for various situations were compared to the Indian national guidelines. Results For DME management, clinicians (ĸ=0.6), AI platforms (ĸ=0.58), and the ‘majority clinician response’ and ‘majority AI response’ (ĸ=0.69) had moderate to substantial inter-rate agreement. The study showed fair to substantial agreement for ocular co-morbidity management between clinicians (ĸ=0.8), AI platforms (ĸ=0.36), and the ‘majority clinician response’ and ‘majority AI response’ (ĸ=0.49). Many of the current study’s recommendations and national clinical guidelines agreed and disagreed. When treating center-involving DME with very good visual acuity, lattice degeneration, renal disease, anaemia, and a recent history of cardiovascular disease, there were clear disagreements. Conclusion For the first time, this study recommends DME management using large language model-based generative AI. The study’s findings could guide in revising the global DME management guidelines.

Details

Language :
English
ISSN :
20569920
Volume :
10
Issue :
1
Database :
Directory of Open Access Journals
Journal :
International Journal of Retina and Vitreous
Publication Type :
Academic Journal
Accession number :
edsdoj.2506867ebc9347e789f467cb4021395b
Document Type :
article
Full Text :
https://doi.org/10.1186/s40942-024-00544-6