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Author's Reply: Critical Limitations in Systematic Reviews of Large Language Models in Health Care
Our analysis helps identify models successfully applied in clinical studies, without aiming at or implying comparison across domains. We direct readers to the excellent recent work by Liu et al [3] for a comparison of lightweight large language models (LLMs) for medical tasks.
We carried out a thorough quality assessment following PRISMA guidelines [4]. This might have escaped the correspondent’s attention, as the details are provided in Multimedia Appendix 2 of our work [1].
J Med Internet Res 2025;27:e82729
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Critical Limitations in Systematic Reviews of Large Language Models in Health Care
Although it provides a comprehensive overview, several limitations undermine its utility for clinical decision-making.
The authors exclude journals below a citation threshold of 13,000, which introduces a publication bias. It excludes innovative research from emerging or specialized journals, as documented in the methodology literature. This is problematic in a rapidly evolving field where important innovations may first appear in newer venues.
J Med Internet Res 2025;27:e81769
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Also, while the benefits of simulation training are well established, there is a paucity of research studying the differences, if any, in the clinical performance of HCPs from HICs and LMICs in simulation-based case scenarios. Understanding the similarities and differences in clinical performance can help identify areas for improvement in health care education and resource allocation in LMICs.
JMIR Med Educ 2025;11:e52332
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Numbers 1 to 8 represent clinical task categories.
One might wish for a generalist clinical LLM acting as an intelligent hub, as envisioned by Moor et al [15] in their concept of generalist medical AI. Such LLMs do not require model parameter updates to perform various clinical tasks but might be required to be trained or fine-tuned using specialized clinical medical knowledge.
J Med Internet Res 2025;27:e71916
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At baseline, participants were sent a link to a survey to complete demographic and clinical characteristic measures. After this was completed, they were then sent step-by-step instructions on how to download and register for the Avicenna Research app (formerly Ethica Data app) on their smartphone. Surveys were programmed to be sent to participants at 9 AM, 1 PM, and 7 PM to provide a view of participants’ quality of life over the course of the day.
J Med Internet Res 2025;27:e57427
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In medicine, recent studies have demonstrated Chat GPT’s potential to support clinical decision-making, summarize complex medical data, and streamline documentation processes. For instance, Chat GPT has been evaluated for its ability to generate discharge summaries, assist in developing differential diagnoses, and simplify patient communication [3-5].
JMIR Med Educ 2025;11:e58801
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Aggregate VS scoring systems such as the early warning score (EWS) have been developed to aid in the identification and risk stratification of patients at risk of or experiencing clinical deterioration. Once identified, clinical response teams, often termed “rapid response teams” (RRTs), may intervene to provide stabilization or transfer to a higher level of care. This identification and response have been described as the afferent and efferent arms, respectively, of rapid response [16].
J Med Internet Res 2025;27:e66347
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This approach is limited by several clinical misconceptions and assumptions: first, the most efficacious anti–heart failure therapies were developed first. The counterargument is exemplified by the fact that digitalis has been used in clinical practice for 200 years, yet it is no longer considered a key therapeutic agent. Second, drug efficacy is only achieved at maximum target doses [1].
JMIR Res Protoc 2025;14:e44027
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Therefore, our results suggest that the AI-enabled therapy support tool may improve individual patients’ outcomes and support clinical services by increasing treatment adherence and treatment effectiveness.
We evaluated the efficacy of an AI-enabled therapy support tool (Limbic Care; Limbic Ltd [25]) in a real-world clinical context in 5 NHS Talking Therapies for anxiety and depression services provided by Everyturn Mental Health in the United Kingdom.
J Med Internet Res 2025;27:e60435
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Patients were decannulated based on clinical observations made by registered nurses and assessments by SLPs. Two patients died during the ICU admission, not related to swallowing abnormalities.
Patient characteristicsa.
a Variables with a normal distribution are described as mean (SD) and variables with a nonnormal distribution are described as median (IQR).
JMIR Serious Games 2025;13:e60685
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