The global enthusiasm surrounding large language models(LLMs)has given rise to a renewed optimism for addressing health inequities,particularly in low-and middle-income countries(LMICs).1 However,despite significant a...The global enthusiasm surrounding large language models(LLMs)has given rise to a renewed optimism for addressing health inequities,particularly in low-and middle-income countries(LMICs).1 However,despite significant advances in AI-driven clinical tools,most innovations remain confined to high-resource settings.LMICs continue to grapple with systemic barriers,including fragmented digital infrastructure,persistent clinician shortages,data scarcity,and severe budgetary constraints.Recent studies have confirmed that DeepSeek-R1 performs on par with leading proprietary models.In a 125-case evaluation,DeepSeek-R1 matched GPT-4o in treatment recommendation accuracy(Likert mean score:4.48 vs.4.70,p=0.1522)and significantly outperformed Gemini-2.0 Flash(p=0.0235).Across a range of diagnostic tasks,DeepSeek demonstrated comparable performance to GPT-4o,thereby substantiating its viability as a cost-effective yet clinically proficient substitute.2,3 In this context,DeepSeek,an open-source,transparent,multilingual,and locally deployable LLM,has emerged as a viable AI solution for under-resourced health systems due to three significant attributes:transparency and explainability,economic accessibility,and local customization and deployment.展开更多
基金funded by the Special Project on Basic Research of Shenzhen Science and Technology Plan in 2022,namely"Social Experiment on the Impact of Artificial Intelligence on Medical Service Models"(project no.20220629202144002).
文摘The global enthusiasm surrounding large language models(LLMs)has given rise to a renewed optimism for addressing health inequities,particularly in low-and middle-income countries(LMICs).1 However,despite significant advances in AI-driven clinical tools,most innovations remain confined to high-resource settings.LMICs continue to grapple with systemic barriers,including fragmented digital infrastructure,persistent clinician shortages,data scarcity,and severe budgetary constraints.Recent studies have confirmed that DeepSeek-R1 performs on par with leading proprietary models.In a 125-case evaluation,DeepSeek-R1 matched GPT-4o in treatment recommendation accuracy(Likert mean score:4.48 vs.4.70,p=0.1522)and significantly outperformed Gemini-2.0 Flash(p=0.0235).Across a range of diagnostic tasks,DeepSeek demonstrated comparable performance to GPT-4o,thereby substantiating its viability as a cost-effective yet clinically proficient substitute.2,3 In this context,DeepSeek,an open-source,transparent,multilingual,and locally deployable LLM,has emerged as a viable AI solution for under-resourced health systems due to three significant attributes:transparency and explainability,economic accessibility,and local customization and deployment.
