ReviewJournal of clinical medicine2024
Large Language Model Prompting Techniques for Advancement in Clinical Medicine.
Review in Journal of clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed.
- Prompt Engineering of Large Language Models for Medication Dose Calculation.Clinical pharmacology and therapeutics · 2026Article
- Adding Artificial Intelligence to the Surgeon's Domain.World journal of surgery · 2026Review
- Multiturn Large Language Model-Based Conversational Agents for Patients With Cancer and Caregivers: Scoping Review.JMIR cancer · 2026Article
- Behavior Change Content and Implementation of Large Language Model-Driven Conversational Agents in Cardiometabolic Care: Scoping Review.Journal of medical Internet research · 2026Article
- Leveraging generative artificial intelligence for the development of non-interventional research study protocols: a proof-of-concept feasibility study.BMC medical research methodology · 2026Article
- Spine patient history-taking: a two phase study of a prompt-engineered large language model assistant.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Algorithmic bias in clinical resource allocation by a large language model: a cross-sectional in-silico evaluation of 13,608 decisions.BMC medical ethics · 2026Article
- Prompt Framing Modulates Safety in Shoulder and Elbow Red-Flag Vignettes: A Large Language Model Study.Diagnostics (Basel, Switzerland) · 2026Article
- Therapeutic Decisions in Inflammatory Bowel Disease: ChatGPT-4.0 Compared to Medical Expertise.JGH open : an open access journal of gastroenterology and hepatology · 2026Article
- Large language models in sports injury care: a comparative expert evaluation of GPT-4o and GPT-5.BMC sports science, medicine & rehabilitation · 2026Article
- Evaluating few-shot prompting for spectrogram-based lung sound classification using a multimodal language model.PLOS digital health · 2026Article
- Use of AI agents to assess preoperative frailty in cancer patients.npj digital surgery · 2026Article
- AI-generated explanations in kidney transplantation: accuracy vs. readability and implications for patient education.Frontiers in artificial intelligence · 2026Article
- Unveiling patterns in clinical data: exploring the role of large language models and clustering algorithms.Frontiers in artificial intelligence · 2026Article
- Comment on: "Evaluation of artificial intelligence use in ankylosing spondylitis with chatgpt-4: patient and physician perspectives".Clinical rheumatology · 2026Article
- A typology of physician input approaches to using AI chatbots for clinical decision-making.NPJ digital medicine · 2025Article
- Evaluation of large language models in patient education for hyperthyroidism: A comparative study of chatgpt, gemini, and deepseek.Endocrine · 2025Article
- Education Research: Can Large Language Models Match MS Specialist Training?: A Comparative Study of AI and Student Responses to Support Neurology Education.Neurology. Education · 2025Article
- Comment on 'AI Chatbots as Sources of STD Information: A Study on Reliability and Readability'.Journal of medical systems · 2025Article
- Digital Cardiovascular Twins, AI Agents, and Sensor Data: A Narrative Review from System Architecture to Proactive Heart Health.Sensors (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large Language Models (LLMs have the potential to revolutionize clinical medicine by enhancing healthcare access, diagnosis, surgical planning, and education. However, their utilization requires careful, prompt engineering to mitigate challenges like hallucinations and biases. Proper utilization of LLMs involves understanding foundational concepts such as tokenization, embeddings, and attention mechanisms, alongside strategic prompting techniques to ensure accurate outputs. For innovative healthcare solutions, it is essential to maintain ongoing collaboration between AI technology and medical professionals. Ethical considerations, including data security and bias mitigation, are critical to their application. By leveraging LLMs as supplementary resources in research and education, we can enhance learning and support knowledge-based inquiries, ultimately advancing the quality and accessibility of medical care. Continued research and development are necessary to fully realize the potential of LLMs in transforming healthcare.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.