ReviewJapanese journal of radiology2024
The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI.
Review in Japanese journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 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
61 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applying transformer-based deep learning models in image-driven cancer diagnosis: a comprehensive bibliometric analysis of global research trends.Frontiers in oncology · 2026Pooled it
- Artificial Intelligence Reporting Guidelines in Radiology: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Large language models in neuroradiology: an international survey of awareness, applications, and concerns.Neuroradiology · 2026Article
- Less Is More: Radiologists Prefer Brief Narratives in LLM-Generated Clinical History Summaries.Journal of imaging informatics in medicine · 2026Article
- Prospective quantitative analysis of hyperparameter and input optimization in GPT-5: comparative contribution to radiologist performance in abdominal radiology.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Large language models and prostate MRI reporting: a stringent testbed for safe deployment under evolving AI, health-data, and cybersecurity regulation.Abdominal radiology (New York) · 2026Review
- Artificial intelligence and pediatric imaging data: ethical strategies for learning and collaboration.Pediatric radiology · 2026Review
- Photon-counting detector computed tomography in thoracic oncology: revolutionizing tumor imaging through precision and detail.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- Large Language Models in Ophthalmology: A Bibliographic Analysis.Turkish journal of ophthalmology · 2026Article
- AgentMRI: A Vison Language Model-Powered AI System for Self-regulating MRI Reconstruction with Multiple Degradations.Journal of imaging informatics in medicine · 2026Article
- Artificial superintelligence alignment in healthcare.Japanese journal of radiology · 2026Review
- Multidimensional evaluation of large language models in radiology report readability.NPJ digital medicine · 2026Article
- Recent Advances in Musculoskeletal Radiology: Bridging Innovation and Clinical Application.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Review
- Large language model agents for biological intelligence across genomics, proteomics, spatial biology, and biomedicine.Briefings in bioinformatics · 2026Review
- Impact of Large Language Model Assistance on Radiologists' Diagnostic Performance for Brain Tumors by Experience Level.Journal of clinical medicine · 2026Article
- A modern radiologist's guide to artificial intelligence.Pediatric radiology · 2026Review
- Effectiveness of Informed AI Use on Clinical Competence of General Practitioners and Internists: Pre-Post Intervention Study.JMIR medical education · 2026Article
- Artificial Intelligence in Radiology: Hidden Fragilities and the Path to Resilience.Saudi medical journal · 2026Review
- Diagnostic Accuracy and Stability of Multimodal Large Language Models for Hand Fracture Detection: A Multi-Run Evaluation on Plain Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- From dictation to diagnosis: enhancing radiology reporting with integrated speech recognition in multimodal large language models.European radiology · 2026Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The advent of Deep Learning (DL) has significantly propelled the field of diagnostic radiology forward by enhancing image analysis and interpretation. The introduction of the Transformer architecture, followed by the development of Large Language Models (LLMs), has further revolutionized this domain. LLMs now possess the potential to automate and refine the radiology workflow, extending from report generation to assistance in diagnostics and patient care. The integration of multimodal technology with LLMs could potentially leapfrog these applications to unprecedented levels.However, LLMs come with unresolved challenges such as information hallucinations and biases, which can affect clinical reliability. Despite these issues, the legislative and guideline frameworks have yet to catch up with technological advancements. Radiologists must acquire a thorough understanding of these technologies to leverage LLMs' potential to the fullest while maintaining medical safety and ethics. This review aims to aid in that endeavor.
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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.