ChatGPT or LLM in next-generation drug discovery and development: Pharmaceutical and biotechnology companies can make use of the artificial intelligence (AI)-based device for a faster way of drug discovery and development

Pal, Soumen; Bhattacharya, Manojit; Islam, Md. Aminul; Chakraborty, Chiranjib

doi:10.1097/js9.0000000000000719

Cited by 14 publications

(1 citation statement)

References 17 publications

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“…In medical education, LLMs not only excel in passing examinations but also revolutionize interactive teaching methods, such as creating patient case studies or generating examination questions . Additionally, their utility in medical research is of great significance, where they contribute to pharmaceutical discovery by identifying potential drug candidates and enhancing literature analysis by summarizing research findings or detecting trends in medical studies . These varied applications highlight the extensive impact of LLMs across the medical spectrum.…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of a Retrieval-Augmented Large Language Model Framework for Ophthalmology

Luo,

Pang,

et al. 2024

JAMA Ophthalmol

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ImportanceAlthough augmenting large language models (LLMs) with knowledge bases may improve medical domain–specific performance, practical methods are needed for local implementation of LLMs that address privacy concerns and enhance accessibility for health care professionals.ObjectiveTo develop an accurate, cost-effective local implementation of an LLM to mitigate privacy concerns and support their practical deployment in health care settings.Design, Setting, and ParticipantsChatZOC (Sun Yat-Sen University Zhongshan Ophthalmology Center), a retrieval-augmented LLM framework, was developed by enhancing a baseline LLM with a comprehensive ophthalmic dataset and evaluation framework (CODE), which includes over 30 000 pieces of ophthalmic knowledge. This LLM was benchmarked against 10 representative LLMs, including GPT-4 and GPT-3.5 Turbo (OpenAI), across 300 clinical questions in ophthalmology. The evaluation, involving a panel of medical experts and biomedical researchers, focused on accuracy, utility, and safety. A double-masked approach was used to try to minimize bias assessment across all models. The study used a comprehensive knowledge base derived from ophthalmic clinical practice, without directly involving clinical patients.ExposuresLLM response to clinical questions.Main Outcomes and MeasuresAccuracy, utility, and safety of LLMs in responding to clinical questions.ResultsThe baseline model achieved a human ranking score of 0.48. The retrieval-augmented LLM had a score of 0.60, a difference of 0.12 (95% CI, 0.02-0.22; P = .02) from baseline and not different from GPT-4 with a score of 0.61 (difference = 0.01; 95% CI, −0.11 to 0.13; P = .89). For scientific consensus, the retrieval-augmented LLM was 84.0% compared with the baseline model of 46.5% (difference = 37.5%; 95% CI, 29.0%-46.0%; P &lt; .001) and not different from GPT-4 with a value of 79.2% (difference = 4.8%; 95% CI, −0.3% to 10.0%; P = .06).Conclusions and RelevanceResults of this quality improvement study suggest that the integration of high-quality knowledge bases improved the LLM’s performance in medical domains. This study highlights the transformative potential of augmented LLMs in clinical practice by providing reliable, safe, and practical clinical information. Further research is needed to explore the broader application of such frameworks in the real world.

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Section: Introductionmentioning

confidence: 99%