A Systematic Review and Meta-Analysis of Artificial Intelligence Tools in Medicine and Healthcare: Applications, Considerations, Limitations, Motivation and Challenges

Diagnostics, Год журнала: 2024, Номер 14(1), С. 109 - 109

Опубликована: Янв. 4, 2024

Artificial intelligence (AI) has emerged as a transformative force in various sectors, including medicine and healthcare. Large language models like ChatGPT showcase AI’s potential by generating human-like text through prompts. ChatGPT’s adaptability holds promise for reshaping medical practices, improving patient care, enhancing interactions among healthcare professionals, patients, data. In pandemic management, rapidly disseminates vital information. It serves virtual assistant surgical consultations, aids dental simplifies education, disease diagnosis. A total of 82 papers were categorised into eight major areas, which are G1: treatment medicine, G2: buildings equipment, G3: parts the human body areas disease, G4: G5: citizens, G6: cellular imaging, radiology, pulse images, G7: doctors nurses, G8: tools, devices administration. Balancing role with judgment remains challenge. systematic literature review using PRISMA approach explored healthcare, highlighting versatile applications, limitations, motivation, challenges. conclusion, diverse applications demonstrate its innovation, serving valuable resource students, academics, researchers Additionally, this study guide, assisting field alike.

Язык: Английский

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Generative artificial intelligence in healthcare: A scoping review on benefits, challenges and applications

International Journal of Medical Informatics, Год журнала: 2024, Номер 188, С. 105474 - 105474

Опубликована: Май 8, 2024

Язык: Английский

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Potential of GPT-4 for Detecting Errors in Radiology Reports: Implications for Reporting Accuracy

Radiology, Год журнала: 2024, Номер 311(1)

Опубликована: Апрель 1, 2024

Background Errors in radiology reports may occur because of resident-to-attending discrepancies, speech recognition inaccuracies, and large workload. Large language models, such as GPT-4 (ChatGPT; OpenAI), assist generating reports. Purpose To assess effectiveness identifying common errors reports, focusing on performance, time, cost-efficiency. Materials Methods In this retrospective study, 200 (radiography cross-sectional imaging [CT MRI]) were compiled between June 2023 December at one institution. There 150 from five error categories (omission, insertion, spelling, side confusion, other) intentionally inserted into 100 the used reference standard. Six radiologists (two senior radiologists, two attending physicians, residents) tasked with detecting these errors. Overall detection categories, reading time assessed using Wald χ2 tests paired-sample t tests. Results (detection rate, 82.7%;124 150; 95% CI: 75.8, 87.9) matched average performance independent their experience (senior 89.3% [134 83.4, 93.3]; 80.0% [120 72.9, 85.6]; residents, P value range, .522–.99). One radiologist outperformed 94.7%; 142 89.8, 97.3; = .006). required less processing per report than fastest human reader study (mean 3.5 seconds ± 0.5 [SD] vs 25.1 20.1, respectively; < .001; Cohen d −1.08). The use resulted lower mean correction cost most cost-efficient ($0.03 0.01 $0.42 0.41; −1.12). Conclusion rate was comparable that potentially reducing work hours cost. © RSNA, 2024 See also editorial by Forman issue.

Язык: Английский

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ChatGPT Provides Unsatisfactory Responses to Frequently Asked Questions Regarding Anterior Cruciate Ligament Reconstruction

Arthroscopy The Journal of Arthroscopic and Related Surgery, Год журнала: 2024, Номер 40(7), С. 2067 - 2079.e1

Опубликована: Фев. 2, 2024

Язык: Английский

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The Era of Artificial Intelligence Deception: Unraveling the Complexities of False Realities and Emerging Threats of Misinformation

Information, Год журнала: 2024, Номер 15(6), С. 299 - 299

Опубликована: Май 23, 2024

This study delves into the dual nature of artificial intelligence (AI), illuminating its transformative potential that has power to revolutionize various aspects our lives. We delve critical issues such as AI hallucinations, misinformation, and unpredictable behavior, particularly in large language models (LLMs) AI-powered chatbots. These technologies, while capable manipulating human decisions exploiting cognitive vulnerabilities, also hold key unlocking unprecedented opportunities for innovation progress. Our research underscores need robust, ethical development deployment frameworks, advocating a balance between technological advancement societal values. emphasize importance collaboration among researchers, developers, policymakers, end users steer toward maximizing benefits minimizing harms. highlights role responsible practices, including regular training, engagement, sharing experiences users, mitigate risks develop best practices. call updated legal regulatory frameworks keep pace with advancements ensure their alignment principles By fostering open dialog, knowledge, prioritizing considerations, we can harness AI’s drive managing inherent challenges.

Язык: Английский

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Systematic Review of Large Language Models for Patient Care: Current Applications and Challenges

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Март 5, 2024

Abstract The introduction of large language models (LLMs) into clinical practice promises to improve patient education and empowerment, thereby personalizing medical care broadening access knowledge. Despite the popularity LLMs, there is a significant gap in systematized information on their use care. Therefore, this systematic review aims synthesize current applications limitations LLMs using data-driven convergent synthesis approach. We searched 5 databases for qualitative, quantitative, mixed methods articles published between 2022 2023. From 4,349 initial records, 89 studies across 29 specialties were included, primarily examining based GPT-3.5 (53.2%, n=66 124 different examined per study) GPT-4 (26.6%, n=33/124) architectures question answering, followed by generation, including text summarization or translation, documentation. Our analysis delineates two primary domains LLM limitations: design output. Design included 6 second-order 12 third-order codes, such as lack domain optimization, data transparency, accessibility issues, while output 9 32 example, non-reproducibility, non-comprehensiveness, incorrectness, unsafety, bias. In conclusion, study first systematically map care, providing foundational framework taxonomy implementation evaluation healthcare settings.

Язык: Английский

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Current applications and challenges in large language models for patient care: a systematic review

Communications Medicine, Год журнала: 2025, Номер 5(1)

Опубликована: Янв. 21, 2025

Abstract Background The introduction of large language models (LLMs) into clinical practice promises to improve patient education and empowerment, thereby personalizing medical care broadening access knowledge. Despite the popularity LLMs, there is a significant gap in systematized information on their use care. Therefore, this systematic review aims synthesize current applications limitations LLMs Methods We systematically searched 5 databases for qualitative, quantitative, mixed methods articles published between 2022 2023. From 4349 initial records, 89 studies across 29 specialties were included. Quality assessment was performed using Mixed Appraisal Tool 2018. A data-driven convergent synthesis approach applied thematic syntheses LLM free line-by-line coding Dedoose. Results show that most investigate Generative Pre-trained Transformers (GPT)-3.5 (53.2%, n = 66 124 different examined) GPT-4 (26.6%, 33/124) answering questions, followed by generation, including text summarization or translation, documentation. Our analysis delineates two primary domains limitations: design output. Design include 6 second-order 12 third-order codes, such as lack domain optimization, data transparency, accessibility issues, while output 9 32 example, non-reproducibility, non-comprehensiveness, incorrectness, unsafety, bias. Conclusions This maps care, providing foundational framework taxonomy implementation evaluation healthcare settings.

Язык: Английский

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DeepSeek versus ChatGPT: Multimodal artificial intelligence revolutionizing scientific discovery. From language editing to autonomous content generation—Redefining innovation in research and practice

Knee Surgery Sports Traumatology Arthroscopy, Год журнала: 2025, Номер unknown

Опубликована: Фев. 12, 2025

Язык: Английский

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ChatGPT is a comprehensive education tool for patients with patellar tendinopathy, but it currently lacks accuracy and readability

Musculoskeletal Science and Practice, Год журнала: 2025, Номер 76, С. 103275 - 103275

Опубликована: Янв. 31, 2025

Generative artificial intelligence tools, such as ChatGPT, are becoming increasingly integrated into daily life, and patients might turn to this tool seek medical information. To evaluate the performance of ChatGPT-4 in responding patient-centered queries for patellar tendinopathy (PT). Forty-eight were collected from online sources, PT patients, experts then submitted ChatGPT-4. Three board-certified independently assessed accuracy comprehensiveness responses. Readability was measured using Flesch-Kincaid Grade Level (FKGL: higher scores indicate a grade reading level). The Patient Education Materials Assessment Tool (PEMAT) evaluated understandability, actionability (0-100%, information with clearer messages more identifiable actions). Semantic Textual Similarity (STS score, 0-1; similarity) variation meaning texts over two months (including ChatGPT-4o) different terminologies related PT. Sixteen (33%) 48 responses rated accurate, while 36 (75%) comprehensive. Only 17% treatment-related questions received accurate Most written at college level (median interquartile range [IQR] FKGL score: 15.4 [14.4-16.6]). median PEMAT understandability 83% (IQR: 70%-92%), actionability, it 60% 40%-60%). medians STS across all ≥ 0.9. provided generally comprehensive response but lacked difficult read individuals below level.

Язык: Английский

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A practical guide to the implementation of artificial intelligence in orthopaedic research—Part 2: A technical introduction

Journal of Experimental Orthopaedics, Год журнала: 2024, Номер 11(3)

Опубликована: Май 7, 2024

Recent advances in artificial intelligence (AI) present a broad range of possibilities medical research. However, orthopaedic researchers aiming to participate research projects implementing AI-based techniques require sound understanding the technical fundamentals this rapidly developing field. Initial sections primer provide an overview general and more detailed taxonomy AI methods. Researchers are presented with basics most frequently performed machine learning (ML) tasks, such as classification, regression, clustering dimensionality reduction. Additionally, spectrum supervision ML including domains supervised, unsupervised, semisupervised self-supervised will be explored. neural networks (NNs) deep (DL) architectures have rendered them essential tools for analysis complex data, which warrants rudimentary introduction researchers. Furthermore, capability natural language processing (NLP) interpret patterns human is discussed may offer several potential applications text patient sentiment clinical decision support. The discussion concludes transformative generative large models (LLMs) on Consequently, second article series aims equip fundamental knowledge required engage interdisciplinary collaboration AI-driven

Язык: Английский

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