A guide to optometrists for appraising and using artificial intelligence in clinical practice

Murphy, Timothy I.; Armitage, James A.; Wijngaarden, Peter van; Abel, Larry A; Douglass, Amanda

doi:10.1080/08164622.2023.2197578

Cited by 3 publications

(2 citation statements)

References 58 publications

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“…The feature localization data presented in this study may help clinicians understand outputs from artificial intelligence systems. Such systems often provide visual explanations of their output in the form of heatmaps [32][33][34]. Localization knowledge may provide insight into the features that are most influential in artificial intelligence system classifications.…”

Section: Discussionmentioning

confidence: 99%

Programmatically Localizing Diabetic Retinopathy Features in 45-Degree Retinal Photographs Using Anatomical Colocation

Murphy,

Douglass,

van Wijngaarden

et al. 2024

JCM

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Background: The aim in this study was to investigate the localization of diabetic retinopathy features at the posterior pole. Methods: This study extracted diabetic retinopathy feature locations from 757 macula-centered 45-degree fundus photographs in the publicly available DDR dataset. Arteriole and venule locations were also extracted from the RITE (n = 35) and IOSTAR (n = 29) datasets. Images were normalized to collocate optic disc and macula positions, and feature positions were collated to generate a frequency distribution matrix. Sørensen–Dice coefficients were calculated to compare the location of different features. Results: Arterioles occurred in two main, distinct arcuate patterns. Venules showed a more diffuse distribution. Microaneurysms were diffusely located around the posterior pole. Hemorrhages and exudates occurred more frequently at the temporal aspect of the macula. Cotton wool spots occurred in a region approximating the radial peripapillary capillaries. Intraretinal microvascular abnormalities and neovascularization were seen throughout the posterior pole, with neovascularization at the disc (n = 65) being more common than neovascularization elsewhere (n = 46). Venous beading occurred primarily between the first and third bifurcations of the venules. Diabetic retinopathy overall was more frequent in the temporal aspect of the macula. The location of cotton wool spots and exudates showed moderate similarity (0.52) when all data were considered, reducing to low similarity (0.18) when areas of low frequency were removed. Conclusions: Diabetic retinopathy occurs throughout the posterior pole but is more frequent in the temporal aspect of the macula. Understanding the location of diabetic retinopathy features may help inform visual search strategies for diabetic retinopathy screening.

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Section: Discussionmentioning

confidence: 99%

Programmatically Localizing Diabetic Retinopathy Features in 45-Degree Retinal Photographs Using Anatomical Colocation

Murphy,

Douglass,

van Wijngaarden

et al. 2024

JCM

Self Cite

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“…12 Even though it is technically possible, AI has not been able to reach its target of converging AI and clinical care so far. 13 NLP, the other subset of AI, is focused on extracting and processing text data that include written and spoken words. NLP could transform human language (free text) or image into code that computers understand, and has been primarily used to date for information retrieval and text extraction.…”

Section: Introductionmentioning

confidence: 99%

Utility of artificial intelligence‐based large language models in ophthalmic care

Biswas,

Davies,

Sheppard

et al. 2024

Ophthalmic Physiologic Optic

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PurposeWith the introduction of ChatGPT, artificial intelligence (AI)‐based large language models (LLMs) are rapidly becoming popular within the scientific community. They use natural language processing to generate human‐like responses to queries. However, the application of LLMs and comparison of the abilities among different LLMs with their human counterparts in ophthalmic care remain under‐reported.Recent FindingsHitherto, studies in eye care have demonstrated the utility of ChatGPT in generating patient information, clinical diagnosis and passing ophthalmology question‐based examinations, among others. LLMs' performance (median accuracy, %) is influenced by factors such as the iteration, prompts utilised and the domain. Human expert (86%) demonstrated the highest proficiency in disease diagnosis, while ChatGPT‐4 outperformed others in ophthalmology examinations (75.9%), symptom triaging (98%) and providing information and answering questions (84.6%). LLMs exhibited superior performance in general ophthalmology but reduced accuracy in ophthalmic subspecialties. Although AI‐based LLMs like ChatGPT are deemed more efficient than their human counterparts, these AIs are constrained by their nonspecific and outdated training, no access to current knowledge, generation of plausible‐sounding ‘fake’ responses or hallucinations, inability to process images, lack of critical literature analysis and ethical and copyright issues. A comprehensive evaluation of recently published studies is crucial to deepen understanding of LLMs and the potential of these AI‐based LLMs.SummaryOphthalmic care professionals should undertake a conservative approach when using AI, as human judgement remains essential for clinical decision‐making and monitoring the accuracy of information. This review identified the ophthalmic applications and potential usages which need further exploration. With the advancement of LLMs, setting standards for benchmarking and promoting best practices is crucial. Potential clinical deployment requires the evaluation of these LLMs to move away from artificial settings, delve into clinical trials and determine their usefulness in the real world.

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Will artificial intelligence render optometrists redundant?

Efron

2023

Clinical and Experimental Optometry

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A guide to optometrists for appraising and using artificial intelligence in clinical practice

Cited by 3 publications

References 58 publications

Programmatically Localizing Diabetic Retinopathy Features in 45-Degree Retinal Photographs Using Anatomical Colocation

Programmatically Localizing Diabetic Retinopathy Features in 45-Degree Retinal Photographs Using Anatomical Colocation

Utility of artificial intelligence‐based large language models in ophthalmic care

Will artificial intelligence render optometrists redundant?

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