ArticleAnnals of biomedical engineering2023
GPT Technology to Help Address Longstanding Barriers to Care in Free Medical Clinics.
Article in Annals of biomedical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Article
- Artificial intelligence in migraine: a narrative review on the diagnostic, prognostic, and therapeutic applications.Oxford open neuroscience · 2026Article
- Review
- Efficacy of large language models and their potential in Obstetrics and Gynecology education.Obstetrics & gynecology science · 2024Article
- ChatGPT and Beyond: An overview of the growing field of large language models and their use in ophthalmology.Eye (London, England) · 2024Review
- Using ChatGPT to promote college students' participation in physical activities and its effect on mental health.World journal of psychiatry · 2024Article
- Artificial Intelligence in Sports Medicine: Could GPT-4 Make Human Doctors Obsolete?Annals of biomedical engineering · 2023Article
- Exploring the Potential of GPT-4 in Biomedical Engineering: The Dawn of a New Era.Annals of biomedical engineering · 2023Article
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
The implementation of technology in healthcare has revolutionized patient-centered decision making by providing contextualized information about a patient's healthcare journey, leading to increased efficiency (Keyworth et al. in BMC Med Inform Decis Mak 18:93, 2018, https://doi.org/10.1186/s12911-018-0661-3 ). Artificial intelligence has been integrated within Electronic Health Records (EHR) to prompt screenings or diagnostic tests based on a patient's holistic health profile. While larger hospitals have already widely adopted these technologies, free clinics hold lower utilization of these advanced capability EHRs. The patient population at a free clinic faces a multitude of factors that limits their access to comprehensive care, thus requiring necessary efforts and measures to close the gap in healthcare disparities. Emerging Artificial Intelligence (AI) technology, such as OpenAI's ChatGPT, GPT-4, and other large language models (LLMs) have remarkable potential to improve patient care outcomes, promote health equity, and enhance comprehensive and holistic care in resource-limited settings. This paper aims to identify areas in which integrating these LLM AI advancements into free clinics operations can optimize and streamline healthcare delivery to underserved patient populations. This paper also identifies areas of improvements in GPT that are necessary to deliver those services.
Indexed as
Identifiers
37355478What 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.