An engineering-oriented, end-to-end roadmap that structures the full lifecycle of clinical language model systems—from model design and domain adaptation to optimization and real-world evaluation is introduced.
Abstract
Large Language Models (LLMs), such as Generative Pre-trained Transformer 4 (GPT-4) and Medical Pathways Language Model (Med-PaLM), have transformed clinical Natural Language Processing (NLP), demonstrating remarkable capabilities in understanding and generating medical text. However, their deployment in healthcare remains constrained by high computational demands, data privacy risks, and limited interpretability. In response, this survey provides a comprehensive synthesis of research on Small Language Models (SLMs) in medicine, introducing an engineering-oriented, end-to-end roadmap that structures the full lifecycle of clinical language model systems—from model design and domain adaptation to optimization and real-world evaluation. We systematically reviewed lightweight architectures, parameter-efficient adaptation strategies, and data-centric training techniques that enable domain-specific optimization under limited computational resources. The survey organizes SLM applications across core NLP tasks—classification, relation extraction, summarization, and question answering—and further examines specialized models designed for domains such as radiology, oncology, ophthalmology, nuclear medicine, and cardiology. A multidimensional evaluation framework was introduced, combining quantitative and qualitative metrics to assess factual accuracy, clinical reliability, and computational efficiency. Finally, we identify ongoing challenges and future directions, emphasizing Retrieval-Augmented Generation (RAG), Reinforcement Learning from Human Feedback (RLHF), and agentic Artificial Intelligence (AI) frameworks that enable language models to support planning, tool use, and workflow-aware clinical decision-making. Collectively, this study establishes a foundation for engineering-driven SLM pipelines that balance efficiency, adaptability, and clinical trustworthiness in real-world healthcare applications. This review is intended for a broad interdisciplinary audience, including clinical NLP researchers, machine learning practitioners, healthcare AI engineers, biomedical informatics specialists, and clinicians seeking practical guidance on deploying efficient and privacy-preserving language technologies.
It is suggested that domain-adapted encoder models may be preferable for similar structured clinical NER settings, although larger and externally validated benchmarks are needed before generalizing to other languages, clinical corpora, model families, or deployment environments.
L. Elvas, Carolina Carvalho· Scientific Reports· 0 citations
This entry-level tutorial aims to equip healthcare professionals with the tools necessary to effectively integrate LLMs into clinical practice, ensuring that these powerful technologies are applied in a safe, reliable, and impactful manner.
Qiao Jin, Nicholas Wan, Robert Leaman et al.· Nature Protocols· 1 citation
Developing artificial intelligence capable of clinical language comprehension and reliable diagnostic reasoning has remained a core challenge in biomedical engineering. While Large Language Models (LLMs) demonstrate significant potential in general natural language processing tasks, their direct application in the medical domain is severely constrained by parametric hallucinations and data silos. This paper introduces an end-to-end, resource-efficient, multilingual speech-driven Question-Answering (QA) framework optimized for localized clinical support. To accommodate deployment on consumer-grade execution environments, we implement Parameter-Efficient Fine-Tuning (PEFT) using Low-Rank Adaptation (LoRA) and 4-bit Quantized LoRA (QLoRA) configurations across open-source 3B and 7B parameter architectures. Human preference alignment is enforced via a stateful Reinforcement Learning with Human Feedback (RLHF) loop applying Proximal Policy Optimization (PPO). Crucially, to mitigate the vulnerabilities of passive information retrieval, we introduce an Active Validation Loop powered by Corrective Retrieval-Augmented Generation (CRAG). This validation engine is decoupled from the model harness using the Model Context Protocol (MCP), standardizing asynchronous lookups across dense vector repositories, clinical guidelines, and real-time electronic health registries.
Misha Patel· International Journal of Sci...· 0 citations
Findings show that clinical LLM explainability has shifted toward fluent generative rationales, but evidence that such explanations reflect model reasoning remains limited, and three regulatory priorities are highlighted: prioritizing explanations that enable independent verification or logic auditing over plausibility-only rationales; preferring inspectable models where regulatory documentation is required; and prospectively validating explanations in clinical workflows before scaling.
Natural language processing (NLP) has emerged as a key focus of AI research for the analysis, interpretation, extraction, summarisation, and generation of human language. The vast amount of unstructured textual data in scientific research, electronic health records, clinical notes, radiology reports, public health documents, and digital health platforms has driven the demand for sophisticated computational tools and techniques capable of extracting structured and actionable knowledge from language. NLP has been greatly advanced by deep learning, which allows for automatic representation learning, understanding context, modeling sequences, and generating large amounts of language by means of structures like CNN, RNN, LSTM, GRU, attention mechanisms, transformers, and large language models. This review aims to present a detailed overview of deep learning-based NLP models, methods, applications, challenges, and future directions, focusing on biomedical informatics, clinical text mining, digital health and biomathematical relevance. It has numerous applications such as biomedical literature mining, named entity recognition, relation extraction, clinical decision support, pharmacovigilance, radiology report generation, public health surveillance, and construction of knowledge graph. The specific focus lies in the application of NLP to identify biological entities, clinical variables and quantitative evidence that can be used to support biomathematical modeling. There are several current challenges such as domain shift, privacy, hallucination, bias, interpretability, and reproducibility. The success of future progress relies on reliable, comprehensible, domain specific and clinically verified NLP systems.
Dr. Pradeep Kumar Atulker, Dr. Rahul Kumar Hindustani, Ravi Shankar Nanduri et al.· Genetics and Molecular Resea...· 0 citations
A thorough review of the developments in LLM technologies, their uses in clinical and administrative settings, as well as their ethical considerations are reviewed to suggest a conceptual structure for responsible implementation that will ensure both technological innovation and patient safety, as well as regulatory compliance and ethical health care practices.
Noah Wright· International Journal of Mod...· 0 citations