Where Medical Statistics Meets Artificial Intelligence DOI
David J. Hunter, Christopher Holmes

New England Journal of Medicine, Journal Year: 2023, Volume and Issue: 389(13), P. 1211 - 1219

Published: Sept. 27, 2023

Challenges at the interface of medical statistics and AI are population inference vs. prediction, generalizability, reproducibility interpretation evidence, stability statistical guarantees.

Language: Английский

ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns DOI Open Access
Malik Sallam

Healthcare, Journal Year: 2023, Volume and Issue: 11(6), P. 887 - 887

Published: March 19, 2023

ChatGPT is an artificial intelligence (AI)-based conversational large language model (LLM). The potential applications of LLMs in health care education, research, and practice could be promising if the associated valid concerns are proactively examined addressed. current systematic review aimed to investigate utility highlight its limitations. Using PRIMSA guidelines, a search was conducted retrieve English records PubMed/MEDLINE Google Scholar (published research or preprints) that context practice. A total 60 were eligible for inclusion. Benefits cited 51/60 (85.0%) included: (1) improved scientific writing enhancing equity versatility; (2) (efficient analysis datasets, code generation, literature reviews, saving time focus on experimental design, drug discovery development); (3) benefits (streamlining workflow, cost saving, documentation, personalized medicine, literacy); (4) education including learning critical thinking problem-based learning. Concerns regarding use stated 58/60 (96.7%) ethical, copyright, transparency, legal issues, risk bias, plagiarism, lack originality, inaccurate content with hallucination, limited knowledge, incorrect citations, cybersecurity infodemics. can induce paradigm shifts However, embrace this AI chatbot should extreme caution considering As it currently stands, does not qualify listed as author articles unless ICMJE/COPE guidelines revised amended. An initiative involving all stakeholders urgently needed. This will help set ethics guide responsible among other academia.

Language: Английский

Citations

1838

Large language models in medicine DOI
Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan

et al.

Nature Medicine, Journal Year: 2023, Volume and Issue: 29(8), P. 1930 - 1940

Published: July 17, 2023

Language: Английский

Citations

1493

Multimodal biomedical AI DOI Open Access
Julián Acosta, Guido J. Falcone, Pranav Rajpurkar

et al.

Nature Medicine, Journal Year: 2022, Volume and Issue: 28(9), P. 1773 - 1784

Published: Sept. 1, 2022

Language: Английский

Citations

583

The next generation of evidence-based medicine DOI Open Access
Vivek Subbiah

Nature Medicine, Journal Year: 2023, Volume and Issue: 29(1), P. 49 - 58

Published: Jan. 1, 2023

Language: Английский

Citations

348

Self-supervised learning in medicine and healthcare DOI
Rayan Krishnan, Pranav Rajpurkar, Eric J. Topol

et al.

Nature Biomedical Engineering, Journal Year: 2022, Volume and Issue: 6(12), P. 1346 - 1352

Published: Aug. 11, 2022

Language: Английский

Citations

323

A foundation model for generalizable disease detection from retinal images DOI Creative Commons
Yukun Zhou, Mark A. Chia, Siegfried Wagner

et al.

Nature, Journal Year: 2023, Volume and Issue: 622(7981), P. 156 - 163

Published: Sept. 13, 2023

Abstract Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis eye diseases systemic disorders 1 . However, development AI models requires substantial annotation are usually task-specific with limited generalizability to different clinical applications 2 Here, we present RETFound, a foundation model that learns generalizable representations from unlabelled provides basis label-efficient adaptation several applications. Specifically, RETFound is trained on 1.6 million by means self-supervised learning then adapted disease detection tasks explicit labels. We show consistently outperforms comparison prognosis sight-threatening diseases, as well incident prediction complex such heart failure myocardial infarction fewer labelled data. solution improve performance alleviate workload experts enable broad imaging.

Language: Английский

Citations

311

The Current and Future State of AI Interpretation of Medical Images DOI
Pranav Rajpurkar, Matthew P. Lungren

New England Journal of Medicine, Journal Year: 2023, Volume and Issue: 388(21), P. 1981 - 1990

Published: May 24, 2023

The authors examine the advantages and limitations of current clinical radiologic AI systems, new workflows, potential effect generative large multimodal foundation models.

Language: Английский

Citations

222

Artificial intelligence chatbots will revolutionize how cancer patients access information: ChatGPT represents a paradigm-shift DOI Creative Commons
Ashley M. Hopkins, Jessica M. Logan, Ganessan Kichenadasse

et al.

JNCI Cancer Spectrum, Journal Year: 2023, Volume and Issue: 7(2)

Published: Feb. 21, 2023

On November 30, 2022, OpenAI enabled public access to ChatGPT, a next-generation artificial intelligence with highly sophisticated ability write, solve coding issues, and answer questions. This communication draws attention the prospect that ChatGPT its successors will become important virtual assistants patients health-care providers. In our assessments, ranging from answering basic fact-based questions responding complex clinical questions, demonstrated remarkable formulate interpretable responses, which appeared minimize likelihood of alarm compared Google's feature snippet. Arguably, use case presents an urgent need for regulators professionals be involved in developing standards minimum quality raise patient awareness current limitations emerging assistants. commentary aims at tipping point paradigm shift.

Language: Английский

Citations

208

The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century DOI Creative Commons
Shiva Maleki Varnosfaderani, Mohamad Forouzanfar

Bioengineering, Journal Year: 2024, Volume and Issue: 11(4), P. 337 - 337

Published: March 29, 2024

As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging a key force transformation. This review motivated by urgent need to harness AI’s potential mitigate these issues aims critically assess integration in different domains. We explore how AI empowers clinical decision-making, optimizes hospital operation management, refines medical image analysis, revolutionizes patient care monitoring through AI-powered wearables. Through several case studies, we has transformed specific domains discuss remaining possible solutions. Additionally, will methodologies assessing solutions, ethical of deployment, importance data privacy bias mitigation responsible technology use. By presenting critical assessment transformative potential, this equips researchers with deeper understanding current future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, technologists navigate complexities implementation, fostering development AI-driven solutions that prioritize standards, equity, patient-centered approach.

Language: Английский

Citations

198

The Utility of ChatGPT as an Example of Large Language Models in Healthcare Education, Research and Practice: Systematic Review on the Future Perspectives and Potential Limitations DOI Creative Commons
Malik Sallam

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Feb. 21, 2023

Abstract An artificial intelligence (AI)-based conversational large language model (LLM) was launched in November 2022 namely, “ChatGPT”. Despite the wide array of potential applications LLMs healthcare education, research and practice, several valid concerns were raised. The current systematic review aimed to investigate possible utility ChatGPT highlight its limitations practice. Using PRIMSA guidelines, a search conducted retrieve English records PubMed/MEDLINE Google Scholar under term Eligibility criteria included published or preprints any type that discussed context A total 280 identified, following full screening, 60 eligible for inclusion. Benefits/applications cited 51/60 (85.0%) with most common being scientific writing followed by benefits (efficient analysis massive datasets, code generation rapid concise literature reviews besides drug discovery development). Benefits practice cost saving, documentation, personalized medicine improved health literacy. Concerns/possible risks use expressed 58/60 (96.7%) ethical issues including risk bias, plagiarism, copyright issues, transparency legal lack originality, incorrect responses, limited knowledge, inaccurate citations. promising which can result paradigm shifts embrace this application should be done extreme caution. Specific education include learning tools shift towards more focus on critical thinking problem-based learning. In valuable streamlining workflow refining medicine. Saving time experimental design enhancing equity versatility are research. Regarding authorship articles, as it currently stands, does not qualify listed an author unless ICMJE/COPE guidelines revised amended. initiative involving all stakeholders involved is urgently needed set ethics conduct responsible practices among other LLMs.

Language: Английский

Citations

196