ReviewJournal of medical systems2024
Transformer Models in Healthcare: A Survey and Thematic Analysis of Potentials, Shortcomings and Risks.
Review in Journal of medical systems, 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, 61 citations in OpenAlex.
- Enhancing healthcare through ontology: a systematic review of challenges and future directions.Frontiers in artificial intelligence · 2026Pooled it
- The Digital Mirror: Clinical Potentials and Relational Risks of Generative AI in Mental Health Interventions.Current psychiatry reports · 2026Review
- From Integrated Care to Learning Systems.Healthcare (Basel, Switzerland) · 2026Review
- Patient and clinician engagement with generative artificial intelligence (GenAI): A scoping review of implications for patient-centered communication.Patient education and counseling · 2026Article
- Sequential pattern transformer (SPT): a generative and interpretable framework for predicting disease trajectories.Neural computing & applications · 2026Article
- Learnable protein representations in computational biology for predicting drug-target affinity.Journal of cheminformatics · 2026Review
- Enhancing Alzheimer's disease classification with a transformer-based model using self-supervised learning.Scientific reports · 2026Article
- Challenges of using generative artificial intelligence for diabetes patient education: a cross-platform analysis of the quality, readability, and actionability of text generated by large language models.Frontiers in public health · 2026Article
- Evaluation of ChatGPT-4o and DeepSeek as tools for orthodontic health literacy in public dental education.Scientific reports · 2025Article
- Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications.World journal of gastroenterology · 2025Review
- Large language models in clinical trials: applications, technical advances, and future directions.BMC medicine · 2025Review
- Big data approaches for novel mechanistic insights on sleep and circadian rhythms: a workshop summary.Sleep · 2025Article
- A Perspective on Artificial Intelligence for Molecular Pathologists.The Journal of molecular diagnostics : JMD · 2025Review
- A systematic review of machine learning applications in predicting opioid associated adverse events.NPJ digital medicine · 2025Article
- Article
- Deep learning architectures for influenza dynamics and treatment optimization: a comprehensive review.Frontiers in artificial intelligence · 2025Review
- Large language models in clinical nutrition: an overview of its applications, capabilities, limitations, and potential future prospects.Frontiers in nutrition · 2025Review
- Deep learning for cardiovascular management: optimizing pathways and cost control under diagnosis-related group models.Frontiers in artificial intelligence · 2025Review
- The potential of DeepSeek for AI-aided diagnosis of antibody-positive autoimmune encephalitis: a single-center, retrospective, observational study.Frontiers in artificial intelligence · 2025Article
- DiaGuide-LLM-Using large language models for patient-specific education and health guidance in diabetes.Frontiers in artificial intelligence · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large Language Models (LLMs) such as General Pretrained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT), which use transformer model architectures, have significantly advanced artificial intelligence and natural language processing. Recognized for their ability to capture associative relationships between words based on shared context, these models are poised to transform healthcare by improving diagnostic accuracy, tailoring treatment plans, and predicting patient outcomes. However, there are multiple risks and potentially unintended consequences associated with their use in healthcare applications. This study, conducted with 28 participants using a qualitative approach, explores the benefits, shortcomings, and risks of using transformer models in healthcare. It analyses responses to seven open-ended questions using a simplified thematic analysis. Our research reveals seven benefits, including improved operational efficiency, optimized processes and refined clinical documentation. Despite these benefits, there are significant concerns about the introduction of bias, auditability issues and privacy risks. Challenges include the need for specialized expertise, the emergence of ethical dilemmas and the potential reduction in the human element of patient care. For the medical profession, risks include the impact on employment, changes in the patient-doctor dynamic, and the need for extensive training in both system operation and data interpretation.
Indexed as
Identifiers
What 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.