首页|Aston University Reports Findings in Artificial Intelligence (Utility of artific ial intelligence-based large language models in ophthalmic care)
Aston University Reports Findings in Artificial Intelligence (Utility of artific ial intelligence-based large language models in ophthalmic care)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Birmingham, Unite d Kingdom, by NewsRx journalists, research stated, "With the introduction of Cha tGPT, artificial intelligence (AI)-based large language models (LLMs) are rapidl y becoming popular within the scientific community. They use natural language pr ocessing to generate human-like responses to queries." The news correspondents obtained a quote from the research from Aston University, "However, the application of LLMs and comparison of the abilities among differ ent LLMs with their human counterparts in ophthalmic care remain under-reported. Hitherto, studies in eye care have demonstrated the utility of ChatGPT in gener ating 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 domai n. Human expert (86%) demonstrated the highest proficiency in disea se diagnosis, while ChatGPT-4 outperformed others in ophthalmology examinations (75.9%), symptom triaging (98%) and providing informat ion and answering questions (84.6%). LLMs exhibited superior perfor mance 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ChatGPT are deemed more efficient than their human counterparts, these AIs are constrained by their nonspecific and outdated train ing, no access to current knowledge, generation of plausible-sounding ‘fake' res ponses or hallucinations, inability to process images, lack of critical literatu re analysis and ethical and copyright issues. A comprehensive evaluation of rece ntly published studies is crucial to deepen understanding of LLMs and the potent ial of these AI-based LLMs. Ophthalmic care professionals should undertake a con servative approach when using AI, as human judgement remains essential for clini cal decision-making and monitoring the accuracy of information. This review iden tified the ophthalmic applications and potential usages which need further explo ration. With the advancement of LLMs, setting standards for benchmarking and pro moting best practices is crucial."
BirminghamUnited KingdomEuropeArti ficial IntelligenceEmerging TechnologiesHealth and MedicineMachine Learnin gOphthalmology
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