The application of large language models in medicine: A scoping review

iScience, Journal Year: 2024, Volume and Issue: 27(5), P. 109713 - 109713

Published: April 23, 2024

This study systematically reviewed the application of large language models (LLMs) in medicine, analyzing 550 selected studies from a vast literature search. LLMs like ChatGPT transformed healthcare by enhancing diagnostics, medical writing, education, and project management. They assisted drafting documents, creating training simulations, streamlining research processes. Despite their growing utility diagnosis improving doctor-patient communication, challenges persisted, including limitations contextual understanding risk over-reliance. The surge LLM-related indicated focus on patient but highlighted need for careful integration, considering validation, ethical concerns, balance with traditional practice. Future directions suggested multimodal LLMs, deeper algorithmic understanding, ensuring responsible, effective use healthcare.

Language: Английский

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ChatGPT as a Virtual Dietitian: Exploring Its Potential as a Tool for Improving Nutrition Knowledge

Applied System Innovation, Journal Year: 2023, Volume and Issue: 6(5), P. 96 - 96

Published: Oct. 23, 2023

The field of health and medical sciences has witnessed a surge published research exploring the applications ChatGPT. However, there remains dearth knowledge regarding its specific potential limitations within domain nutrition. Given increasing prevalence nutrition-related diseases, is critical need to prioritize promotion comprehensive understanding This paper examines utility ChatGPT as tool for improving nutrition knowledge. Specifically, it scrutinizes characteristics in relation personalized meal planning, dietary advice guidance, food intake tracking, educational materials, other commonly found features applications. Additionally, explores support each stage Nutrition Care Process. Addressing prevailing question whether can replace healthcare professionals, this elucidates substantial context practice education. These encompass factors such incorrect responses, coordinated services, hands-on demonstration, physical examination, verbal non-verbal cues, emotional psychological aspects, real-time monitoring feedback, wearable device integration, ethical privacy concerns have been highlighted. In summary, holds promise valuable enhancing knowledge, but further development are needed optimize capabilities domain.

Language: Английский

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Effective Integration of Artificial Intelligence in Medical Education

Advances in healthcare information systems and administration book series, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 19

Published: Feb. 9, 2024

With the increasing popularity of artificial intelligence (AI) applications in medical practices, integration AI technologies into education has garnered significant attention. However, there exists a noticeable research gap when it comes to providing comprehensive guidelines and recommendations for its successful this domain. Addressing is crucial as responsible effective incorporation not only ensures that current future healthcare professionals are well-prepared demands modern medicine but also upholds ethical standards, maximizes potential benefits AI, minimizes risks. The objective chapter fill by offering practical tips actionable insights incorporating education, encompassing practical, ethical, pedagogical, professional implications. Consequently, equips educators learners alike with knowledge tools necessary navigate evolving landscape age AI.

Language: Английский

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Navigating the Future: The Transformative Impact of Artificial Intelligence on Hospital Management- A Comprehensive Review

Cureus, Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 20, 2024

This comprehensive review explores the transformative impact of artificial intelligence (AI) on hospital management, delving into its applications, challenges, and future trends. Integrating AI in administrative functions, clinical operations, patient engagement holds significant promise for enhancing efficiency, optimizing resource allocation, revolutionizing care. However, this evolution is accompanied by ethical, legal, operational considerations that necessitate careful navigation. The underscores key findings, emphasizing implications management. It calls a proactive approach, urging stakeholders to invest education, prioritize ethical guidelines, foster collaboration, advocate thoughtful regulation, embrace culture innovation. healthcare industry can successfully navigate era through collective action, ensuring contributes more effective, accessible, patient-centered delivery.

Language: Английский

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Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare

Integrative Medicine Research, Journal Year: 2024, Volume and Issue: 13(1), P. 101024 - 101024

Published: Feb. 9, 2024

The convergence of traditional, complementary, and integrative medicine (TCIM) with artificial intelligence (AI) is a promising frontier in healthcare. TCIM patient-centric approach that combines conventional complementary therapies, emphasizing holistic well-being. AI can revolutionize healthcare through data-driven decision-making personalized treatment plans. This article explores how technologies complement enhance TCIM, aligning the shared objectives researchers from both fields improving patient outcomes, enhancing care quality, promoting wellness. integration introduces exciting opportunities but also noteworthy challenges. may augment by assisting early disease detection, providing plans, predicting health trends, engagement. Challenges at intersection include data privacy security, regulatory complexities, maintaining human touch patient-provider relationships, mitigating bias algorithms. Patients' trust, informed consent, legal accountability are all essential considerations. Future directions AI-enhanced advanced medicine, understanding efficacy herbal remedies, studying interactions. Research on mitigation, acceptance, trust AI-driven crucial. In this article, we outlined merging holds great promise delivery, personalizing preventive care, Addressing challenges fostering collaboration between experts, practitioners, policymakers, however, vital to harnessing full potential integration.

Language: Английский

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AI and professional liability assessment in healthcare. A revolution in legal medicine?

Frontiers in Medicine, Journal Year: 2024, Volume and Issue: 10

Published: Jan. 8, 2024

The adoption of advanced artificial intelligence (AI) systems in healthcare is transforming the healthcare-delivery landscape. Artificial may enhance patient safety and improve outcomes, but it presents notable ethical legal dilemmas. Moreover, as AI streamlines analysis multitude factors relevant to malpractice claims, including informed consent, adherence standards care, causation, evaluation professional liability might also benefit from its use. Beginning with an basic steps assessing liability, this article examines potential new medical-legal issues that expert witness encounter when analyzing cases integration context. These changes related use integrated AI, will necessitate efforts on part judges, experts, clinicians, require legislative regulations. A be likely necessary cases. On one hand, support witness; however, other introduce specific elements into activities workers. a specialized cultural background. Examining assessment indicates path for medicine involves role collaborative tool. combination human judgment these assessments can comprehensiveness fairness. However, imperative adopt cautious balanced approach prevent complete automation field.

Language: Английский

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Performance of Google’s Artificial Intelligence Chatbot “Bard” (Now “Gemini”) on Ophthalmology Board Exam Practice Questions

Cureus, Journal Year: 2024, Volume and Issue: unknown

Published: March 31, 2024

Purpose: To assess the performance of "Bard," one ChatGPT's competitors, in answering practice questions for ophthalmology board certification exam. Methods: In December 2023, 250 multiple-choice from "BoardVitals" exam question bank were randomly selected and entered into Bard to artificial intelligence chatbot's ability comprehend, process, answer complex scientific clinical ophthalmic questions. A random mix text-only image-and-text 10 subsections. Each subsection included 25 The percentage correct responses was calculated per section, an overall assessment score determined. Results: On average, answered 62.4% (156/250) correctly. worst 24% (6/25) on topic "Retina Vitreous," best "Oculoplastics," with a 84% (21/25). While majority minimal difficulty, not all could be processed by Bard. This particularly issue that human images multiple visual files. Some vignette-style also understood therefore omitted. Future investigations will focus having more increase available data points. Conclusions: correctly is capable analyzing vast amounts medical data, it ultimately lacks holistic understanding experience-informed knowledge ophthalmologist. An ophthalmologist's synthesize diverse pieces information draw experience standardized at present irreplaceable, intelligence, its current form, can employed as valuable tool supplementing clinicians' study methods.

Language: Английский

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The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency

Health Science Reports, Journal Year: 2025, Volume and Issue: 8(1)

Published: Jan. 1, 2025

Artificial Intelligence (AI) beginning to integrate in healthcare, is ushering a transformative era, impacting diagnostics, altering personalized treatment, and significantly improving operational efficiency. The study aims describe AI including important technologies like robotics, machine learning (ML), deep (DL), natural language processing (NLP), investigate how these are used patient interaction, predictive analytics, remote monitoring. goal of this review present thorough analysis AI's effects on healthcare while providing stakeholders with road map for navigating changing environment. This analyzes the impact using data from Web Science (2014-2024), focusing keywords AI, ML, applications. It examines uses by synthesizing recent literature real-world case studies, such as Google Health IBM Watson Health, highlighting technologies, their useful applications, difficulties putting them into practice, problems security resource limitations. also discusses new developments they can affect society. findings demonstrate enhancing skills medical professionals, diagnosis, opening door more individualized treatment plans, reflected steady rise AI-related publications 158 articles (3.54%) 2014 731 (16.33%) 2024. Core applications monitoring analytics improve effectiveness involvement. However, there major obstacles mainstream implementation issues budget constraints. Healthcare may be transformed but its successful use requires ethical responsible use. To meet demands sector guarantee application evaluation highlights necessity ongoing research, instruction, multidisciplinary cooperation. In future, integrating responsibly will essential optimizing advantages reducing related dangers.

Language: Английский

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Can Large Language Models Aid Caregivers of Pediatric Cancer Patients in Information Seeking? A Cross‐Sectional Investigation

Cancer Medicine, Journal Year: 2025, Volume and Issue: 14(1)

Published: Jan. 1, 2025

ABSTRACT Purpose Caregivers in pediatric oncology need accurate and understandable information about their child's condition, treatment, side effects. This study assesses the performance of publicly accessible large language model (LLM)‐supported tools providing valuable reliable to caregivers children with cancer. Methods In this cross‐sectional study, we evaluated four LLM‐supported tools—ChatGPT (GPT‐4), Google Bard (Gemini Pro), Microsoft Bing Chat, SGE—against a set frequently asked questions (FAQs) derived from Children's Oncology Group Family Handbook expert input (In total, 26 FAQs 104 generated responses). Five experts assessed LLM responses using measures including accuracy, clarity, inclusivity, completeness, clinical utility, overall rating. Additionally, content quality was readability, AI disclosure, source credibility, resource matching, originality. We used descriptive analysis statistical tests Shapiro–Wilk, Levene's, Kruskal–Wallis H ‐tests, Dunn's post hoc for pairwise comparisons. Results ChatGPT shows high when by experts. also performed well, especially accuracy clarity responses, whereas Chat SGE had lower scores. Regarding disclosure being AI, it observed less which may have affected maintained balance between response clarity. most readable answered complexity. varied significantly ( p < 0.001) across all evaluations except inclusivity. Through our thematic free‐text comments, emotional tone empathy emerged as unique theme mixed feedback on expectations be empathetic. Conclusion can enhance caregivers' knowledge oncology. Each has strengths areas improvement, indicating careful selection based specific contexts. Further research is required explore application other medical specialties patient demographics, assessing broader applicability long‐term impacts.

Language: Английский

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Evaluating the Efficacy of ChatGPT in Navigating the Spanish Medical Residency Entrance Examination (MIR): Promising Horizons for AI in Clinical Medicine

Clinics and Practice, Journal Year: 2023, Volume and Issue: 13(6), P. 1460 - 1487

Published: Nov. 20, 2023

The rapid progress in artificial intelligence, machine learning, and natural language processing has led to increasingly sophisticated large models (LLMs) for use healthcare. This study assesses the performance of two LLMs, GPT-3.5 GPT-4 models, passing MIR medical examination access specialist training Spain. Our objectives included gauging model's overall performance, analyzing discrepancies across different specialties, discerning between theoretical practical questions, estimating error proportions, assessing hypothetical severity errors committed by a physician.We studied 2022 Spanish results after excluding those questions requiring image evaluations or having acknowledged errors. remaining 182 were presented LLM English. Logistic regression analyzed relationships question length, sequence, performance. We also 23 with images, using GPT-4's new analysis capability.GPT-4 outperformed GPT-3.5, scoring 86.81% (p < 0.001). English translations had slightly enhanced scored 26.1% images worse when Spanish, 13.0%, although differences not statistically significant = 0.250). Among achieved 100% correct response rate several areas, Pharmacology, Critical Care, Infectious Diseases specialties showed lower revealed that while 13.2% existed, gravest categories, such as "error intervention sustain life" resulting death", 0% rate.GPT-4 performs robustly on examination, varying capabilities discriminate knowledge specialties. While high success is commendable, understanding critical, especially considering AI's potential role real-world practice its implications patient safety.

Language: Английский

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