ChatGPT and GPT-4 in Ophthalmology: Applications of Large Language Model Artificial Intelligence in Retina

Ong, Joshua; Hariprasad, Seenu M.; Chhablani, Jay

doi:10.3928/23258160-20230926-01

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…Despite mixed results in ophthalmology, LLM appears to have potential for use in eye health care applications. LLM-based generative AI with ChatGPT3.5v and ChatGPT 4.0v has been used in retina for a variety of indications, including ICD for various case encounters [ 30 , 31 ]. The use of LLM-based generative AI for DME management recommendations in the presence of other ocular co-morbidities has yet to be investigated.…”

Section: Discussionmentioning

confidence: 99%

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

Choudhary,

Gopalakrishnan,

Joshi

et al. 2024

Int J Retin Vitr

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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.

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Section: Discussionmentioning

confidence: 99%

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

Choudhary,

Gopalakrishnan,

Joshi

et al. 2024

Int J Retin Vitr

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“…Although reported performance in ophthalmology has been mixed, LLM appear to have potential for use in eye health care applications. LLM-based generative AI with ChatGPT and ChatGPT 4.0v has been used in retina for a variety of indications, including International Coding of Diseases (ICD) for various case encounters [ 27 , 28 ]. AI’s current role in DR is limited to preventive care, i.e., screening [ 14 , 17 ].…”

Section: Discussionmentioning

confidence: 99%

Recommendations for initial diabetic retinopathy screening of diabetic patients using large language model-based artificial intelligence in real-life case scenarios

Gopalakrishnan,

Joshi,

Chhablani

et al. 2024

Int J Retin Vitr

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Purpose To study the role of artificial intelligence (AI) to identify key risk factors for diabetic retinopathy (DR) screening and develop recommendations based on clinician and large language model (LLM) based AI platform opinions for newly detected diabetes mellitus (DM) cases. Methods Five clinicians and three AI applications were given 20 AI-generated hypothetical case scenarios to assess DR screening timing. We calculated inter-rater agreements between clinicians, AI-platforms, and the “majority clinician response” (defined as the maximum number of identical responses provided by the clinicians) and “majority AI-platform” (defined as the maximum number of identical responses among the 3 distinct AI). Scoring was used to identify risk factors of different severity. Three, two, and one points were given to risk factors requiring screening immediately, within a year, and within five years, respectively. After calculating a cumulative screening score, categories were assigned. Results Clinicians, AI platforms, and the “majority clinician response” and “majority AI response” had fair inter-rater reliability (k value: 0.21–0.40). Uncontrolled DM and systemic co-morbidities required immediate screening, while family history of DM and a co-existing pregnancy required screening within a year. The absence of these risk factors required screening within 5 years of DM diagnosis. Screening scores in this study were between 0 and 10. Cases with screening scores of 0–2 needed screening within 5 years, 3–5 within 1 year, and 6–12 immediately. Conclusion Based on the findings of this study, AI could play a critical role in DR screening of newly diagnosed DM patients by developing a novel DR screening score. Future studies would be required to validate the DR screening score before it could be used as a reference in real-life clinical situations. Clinical trial registration Not applicable.

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“…The integration of large language models (LLMs) like ChatGPT into ophthalmology offers promising advancements ( 29 ). These AI systems could improve diagnostics and patient education through real-time data analysis and access to vast research resources.…”

Section: Limitations Challenges and Future Aspectsmentioning

confidence: 99%

Transforming the future of ophthalmology: artificial intelligence and robotics’ breakthrough role in surgical and medical retina advances: a mini review

Chatzimichail,

Feltgen,

Motta

et al. 2024

Front. Med.

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Over the past decade, artificial intelligence (AI) and its subfields, deep learning and machine learning, have become integral parts of ophthalmology, particularly in the field of ophthalmic imaging. A diverse array of algorithms has emerged to facilitate the automated diagnosis of numerous medical and surgical retinal conditions. The development of these algorithms necessitates extensive training using large datasets of retinal images. This approach has demonstrated a promising impact, especially in increasing accuracy of diagnosis for unspecialized clinicians for various diseases and in the area of telemedicine, where access to ophthalmological care is restricted. In parallel, robotic technology has made significant inroads into the medical field, including ophthalmology. The vast majority of research in the field of robotic surgery has been focused on anterior segment and vitreoretinal surgery. These systems offer potential improvements in accuracy and address issues such as hand tremors. However, widespread adoption faces hurdles, including the substantial costs associated with these systems and the steep learning curve for surgeons. These challenges currently constrain the broader implementation of robotic surgical systems in ophthalmology. This mini review discusses the current research and challenges, underscoring the limited yet growing implementation of AI and robotic systems in the field of retinal conditions.

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ChatGPT and GPT-4 in Ophthalmology: Applications of Large Language Model Artificial Intelligence in Retina

Cited by 7 publications

References 26 publications

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

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

Recommendations for initial diabetic retinopathy screening of diabetic patients using large language model-based artificial intelligence in real-life case scenarios

Transforming the future of ophthalmology: artificial intelligence and robotics’ breakthrough role in surgical and medical retina advances: a mini review

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