ReviewFrontiers in radiology2023
Surviving ChatGPT in healthcare.
Review in Frontiers in radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- Proposed Context-of-Use Evaluation Framework for Medication Management Tasks Completed by Generative Artificial Intelligence.medRxiv : the preprint server for health sciences · 2026Article
- Large Language Model-Assisted Shared Decision-Making (SDM) in Rhinology: A Randomized Controlled Pilot Study.Laryngoscope investigative otolaryngology · 2026Article
- Shaping AI in Pelvic Floor Physiotherapy: The Impact of Role-Play Prompting on ChatGPT Response Quality.International urogynecology journal · 2026Article
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Evaluating the Accuracy, Usefulness, and Safety of ChatGPT for Caregivers Seeking Information on Congenital Muscular Torticollis.Healthcare (Basel, Switzerland) · 2026Article
- A predictive model for differentiating PFA and PFB subtypes of posterior fossa ependymoma using multi-sequence MRI radiomics: a two-center study.Neuroradiology · 2026Article
- Artificial intelligence versus human expertise: reliability of ChatGPT and the London atlas for dental age estimation using panoramic radiographs.BMC oral health · 2025Article
- Using large language models to automate summarization of CT simulation orders in radiation oncology.Journal of applied clinical medical physics · 2025Article
- Artificial intelligence in dental education: can AI-based chatbots compete with general practitioners?BMC medical education · 2025Article
- Assessing ChatGPT's Educational Potential in Lung Cancer Radiotherapy From Clinician and Patient Perspectives: Content Quality and Readability Analysis.JMIR cancer · 2025Article
- Large language models versus traditional textbooks: optimizing learning for plastic surgery case preparation.BMC medical education · 2025Article
- Evaluating large language models as an educational tool for meningioma patients: patient and clinician perspectives.Radiation oncology (London, England) · 2025Article
- ChatGPT-4o and 4o1 Preview as Dietary Support Tools in a Real-World Medicated Obesity Program: A Prospective Comparative Analysis.Healthcare (Basel, Switzerland) · 2025Article
- Enhancing ophthalmology students' awareness of retinitis pigmentosa: assessing the efficacy of ChatGPT in AI-assisted teaching of rare diseases-a quasi-experimental study.Frontiers in medicine · 2025Article
- A recent evaluation on the performance of LLMs on radiation oncology physics using questions of randomly shuffled options.Frontiers in oncology · 2025Article
- Article
- Exploring the potential of large language model-based chatbots in challenges of ribosome profiling data analysis: a review.Briefings in bioinformatics · 2024Review
- Using ChatGPT in Nursing: Scoping Review of Current Opinions.JMIR medical education · 2024Article
- Health is beyond genetics: on the integration of lifestyle and environment in real-time for hyper-personalized medicine.Frontiers in public health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
At the dawn of of Artificial General Intelligence (AGI), the emergence of large language models such as ChatGPT show promise in revolutionizing healthcare by improving patient care, expanding medical access, and optimizing clinical processes. However, their integration into healthcare systems requires careful consideration of potential risks, such as inaccurate medical advice, patient privacy violations, the creation of falsified documents or images, overreliance on AGI in medical education, and the perpetuation of biases. It is crucial to implement proper oversight and regulation to address these risks, ensuring the safe and effective incorporation of AGI technologies into healthcare systems. By acknowledging and mitigating these challenges, AGI can be harnessed to enhance patient care, medical knowledge, and healthcare processes, ultimately benefiting society as a whole.
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.