ArticleAnnals of biomedical engineering2024
Potential for GPT Technology to Optimize Future Clinical Decision-Making Using Retrieval-Augmented Generation.
Article in Annals of biomedical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating the Performance of ChatGPT on Board-Style Examination Questions in Ophthalmology: A Meta-Analysis.Journal of medical systems · 2025Pooled it
- Enhancing patient care with AI agents: integrating advanced chatbot technologies for improved healthcare delivery.BMC medical informatics and decision making · 2026Article
- Large Language Models in Injury Prediction Tools: Simplifying User Interactions and Improving Risk Interpretation.Annals of biomedical engineering · 2026Article
- Improving TCM question answering through tree-organized self-reflective retrieval with LLMs.Frontiers in medicine · 2026Article
- Artificial intelligence in occupational therapy: Comparing ChatGPT and occupational therapist approaches in assessment and intervention.The British journal of occupational therapy · 2026Article
- Multi-step retrieval and reasoning improves radiology question answering with large language models.NPJ digital medicine · 2025Article
- Enhancing Large Language Models for Improved Accuracy and Safety in Medical Question Answering: Comparative Study.JMIR medical education · 2025Article
- RadioRAG: Online Retrieval-Augmented Generation for Radiology Question Answering.Radiology. Artificial intelligence · 2025Article
- Retrieval augmented generation for large language models in healthcare: A systematic review.PLOS digital health · 2025Article
- Thyro-GenAI: A Chatbot Using Retrieval-Augmented Generative Models for Personalized Thyroid Disease Management.Journal of clinical medicine · 2025Article
- A Transformer-Based Pipeline for German Clinical Document De-Identification.Applied clinical informatics · 2025Article
- Enhancing treatment decision-making for low back pain: a novel framework integrating large language models with retrieval-augmented generation technology.Frontiers in medicine · 2025Article
- Explainable AI in medicine: challenges of integrating XAI into the future clinical routine.Frontiers in radiology · 2025Review
- Walking Backward to Ensure Risk Management of Large Language Models in Medicine.The Journal of law, medicine & ethics : a journal of the American Society of Law, Medicine & Ethics · 2025Article
- Information Extraction from Clinical Texts with Generative Pre-trained Transformer Models.International journal of medical sciences · 2025Article
- Use of artificial intelligence for liver diseases: A survey from the EASL congress 2024.JHEP reports : innovation in hepatology · 2024Review
- Article
- The potential of ChatGPT in medicine: an example analysis of nephrology specialty exams in Poland.Clinical kidney journal · 2024Article
- Natural Language Processing for Digital Health in the Era of Large Language Models.Yearbook of medical informatics · 2024Review
- ChatGPT and Beyond: An overview of the growing field of large language models and their use in ophthalmology.Eye (London, England) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advancements in artificial intelligence (AI) provide many helpful tools for healthcare, one of which includes AI chatbots that use natural language processing to create humanlike, conversational dialog. These chatbots have general cognitive skills and are able to engage with clinicians and patients to discuss patients' health conditions and what they may be at risk for. While chatbot engines have access to a wide range of medical texts and research papers, they currently provide high-level, generic responses and are limited in their ability to provide diagnostic guidance and clinical advice to patients on an individual level. The essay discusses the use of retrieval-augmented generation (RAG), which can be used to improve the specificity of user-entered prompts and thereby enhance the detail in AI chatbot responses. By embedding more recent clinical data and trusted medical sources, such as clinical guidelines, into the chatbot models, AI chatbots can provide more patient-specific guidance, faster diagnoses and treatment recommendations, and greater improvement of patient outcomes.
Indexed as
Identifiers
37530906What OpenQuestion 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 OpenQuestion graph.