A non-peer-reviewed letter published in the Journal of Medical Internet Research on June 18, 2026, by Zablah, Molina, and Garcia-Loureiro comments on Zhang et al.'s 2025 review of LLMs in healthcare. The authors benchmark three smaller domain-specific models - Clinical Camel (LLaMA-2-13B), PMC-LLaMA 13B, and Meditron-3 (Qwen2.5-14B) - against GPT-4 on differential diagnosis tasks, finding that the smaller models achieve 85-90% of GPT-4 diagnostic accuracy while using only approximately 15% of its computational resources. The letter argues that current clinical LLM deployment faces practical barriers including 2-10 second inference latency, 16-80+ GB VRAM requirements, and scalability challenges that make equitable deployment across diverse healthcare settings - particularly in low- and middle-income countries - a major unresolved problem.
What it is
A correspondence letter published in the Journal of Medical Internet Research (JMIR) by Isaac Zablah (National Autonomous University of Honduras), Yolly Molina (UNAH), and Antonio Garcia-Loureiro (Universidade de Santiago de Compostela) commenting on Zhang K et al.'s 2025 JMIR review titled 'Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine' (e59069). The letter is non-peer-reviewed and was published June 18, 2026 (doi:10.2196/85726).
Key benchmarking result
The authors conducted an initial benchmarking of three smaller domain-specific LLMs on differential diagnosis tasks against GPT-4 as a baseline. Clinical Camel (LLaMA-2-13B), PMC-LLaMA 13B, and Meditron-3 (Qwen2.5-14B) were evaluated. The finding: models with approximately 14 billion parameters fine-tuned on medical corpora achieved 85-90% of GPT-4 diagnostic accuracy while using only about 15% of GPT-4's computational resources. The authors characterize this as 'considerable room for improvement' in making medical LLMs accessible.
Infrastructure barriers identified
The letter focuses on three practical deployment constraints that it argues Zhang et al. underemphasized. Inference latency currently runs 2-10 seconds per query - potentially too slow for time-critical settings such as emergency triage or intraoperative decision support. Memory footprint requirements of 16-80+ GB VRAM put advanced models out of reach for many healthcare facilities, especially in low- and middle-income countries. And serving hundreds of concurrent clinical users requires distributed architectures and load-balancing strategies not addressed in the original review.
Recommendations
The authors propose model quantization and pruning (50-75% size reduction with minimal accuracy loss to enable deployment on consumer-grade hardware), edge computing deployment using optimized 7-13B parameter models to address data privacy and latency, and hybrid architectures combining lightweight edge models for routine queries with full cloud-based models for complex cases. They also advocate for federated learning strategies that enable training without centralizing sensitive patient data.
Broader call to action
The letter calls for standardized evaluation metrics that go beyond diagnostic accuracy to include operations per diagnosis (computational cost), energy consumption per inference (environmental impact), and cost-effectiveness ratios (accuracy per dollar of infrastructure). The authors argue that the 'transformative potential' described in the Zhang et al. review will only be realized if LLMs can be deployed efficiently and equitably across diverse healthcare environments.
Scoring Rationale #
A non-peer-reviewed correspondence letter with practical benchmarking data showing smaller medical LLMs are compute-competitive with GPT-4, relevant to practitioners building or deploying clinical AI. Limited scope as a brief letter on a 2025 review, and results are from a small initial benchmarking rather than a full study.
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