OpenAI o1-Preview vs. ChatGPT in Healthcare: A New Frontier in Medical AI Reasoning.
| Autor: | Temsah MH; Pediatric Intensive Care Unit, Pediatric Department, College of Medicine, King Saud University Medical City, King Saud University, Riyadh, SAU., Jamal A; Family and Community Medicine Department, King Saud University, Riyadh, SAU., Alhasan K; Pediatric Nephrology Department, King Saud University, Riyadh, SAU., Temsah AA; Dental Department, Specialized Medical Center Hospital, Riyadh, SAU., Malki KH; College of Medicine, King Saud University, Riyadh, SAU. |
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| Jazyk: | angličtina |
| Zdroj: | Cureus [Cureus] 2024 Oct 01; Vol. 16 (10), pp. e70640. Date of Electronic Publication: 2024 Oct 01 (Print Publication: 2024). |
| DOI: | 10.7759/cureus.70640 |
| Abstrakt: | This editorial explores the recent advancements in generative artificial intelligence with the newly-released OpenAI o1-Preview, comparing its capabilities to the traditional ChatGPT (GPT-4) model, particularly in the context of healthcare. While ChatGPT has shown many applications for general medical advice and patient interactions, OpenAI o1-Preview introduces new features with advanced reasoning skills using a chain of thought processes that could enable users to tackle more complex medical queries such as genetic disease discovery, multi-system or complex disease care, and medical research support. The article explores some of the new model's potential and other aspects that may affect its usage, like slower response times due to its extensive reasoning approach yet highlights its potential for reducing hallucinations and offering more accurate outputs for complex medical problems. Ethical challenges, data diversity, access equity, and transparency are also discussed, identifying key areas for future research, including optimizing the use of both models in tandem for healthcare applications. The editorial concludes by advocating for collaborative exploration of all large language models (LLMs), including the novel OpenAI o1-Preview , to fully utilize their transformative potential in medicine and healthcare delivery. This model, with its advanced reasoning capabilities, presents an opportunity to empower healthcare professionals, policymakers, and computer scientists to work together in transforming patient care, accelerating medical research, and enhancing healthcare outcomes. By optimizing the use of several LLM models in tandem, healthcare systems may enhance efficiency and precision, as well as mitigate previous LLM challenges, such as ethical concerns, access disparities, and technical limitations, steering to a new era of artificial intelligence (AI)-driven healthcare. Competing Interests: Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work. (Copyright © 2024, Temsah et al.) |
| Databáze: | MEDLINE |
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