Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts DOI Creative Commons
Dave Van Veen, Cara Van Uden, Louis Blankemeier

et al.

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Oct. 30, 2023

Abstract Sifting through vast textual data and summarizing key information from electronic health records (EHR) imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown immense promise in natural processing (NLP) tasks, efficacy diverse range of clinical summarization tasks has not yet been rigorously demonstrated. In this work, we apply domain adaptation methods to eight LLMs, spanning six datasets four distinct tasks: radiology reports, patient questions, progress notes, doctor-patient dialogue. Our thorough quantitative assessment reveals trade-offs between addition instances where recent advances LLMs may improve results. Further, reader study with ten physicians, show that summaries our best-adapted are preferable human terms completeness correctness. ensuing qualitative analysis highlights challenges faced by both experts. Lastly, correlate traditional NLP metrics scores enhance understanding these align physician preferences. research marks the first evidence outperforming experts text across multiple tasks. This implies integrating into workflows could alleviate documentation burden, empowering focus more personalized care inherently aspects medicine.

Language: Английский

A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly DOI Creative Commons
Yifan Yao, Jinhao Duan, Kaidi Xu

et al.

High-Confidence Computing, Journal Year: 2024, Volume and Issue: 4(2), P. 100211 - 100211

Published: March 1, 2024

Language: Английский

Citations

217
Adapted large language models can outperform medical experts in clinical text summarization DOI
Dave Van Veen, Cara Van Uden, Louis Blankemeier

et al.

Nature Medicine, Journal Year: 2024, Volume and Issue: 30(4), P. 1134 - 1142

Published: Feb. 27, 2024

Language: Английский

Citations

157
A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges DOI Creative Commons
Mohaimenul Azam Khan Raiaan, Md. Saddam Hossain Mukta, Kaniz Fatema

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 26839 - 26874

Published: Jan. 1, 2024

Large Language Models (LLMs) recently demonstrated extraordinary capability, including natural language processing (NLP), translation, text generation, question answering, etc. Moreover, LLMs are a new and essential part of computerized processing, having the ability to understand complex verbal patterns generate coherent appropriate replies for situation. Though this success has prompted substantial increase in research contributions, rapid growth made it difficult overall impact these improvements. Since lot on is coming out quickly, getting tough get an overview all them short note. Consequently, community would benefit from but thorough review recent changes area. This article thoroughly overviews LLMs, their history, architectures, transformers, resources, training methods, applications, impacts, challenges, paper begins by discussing fundamental concepts with its traditional pipeline phase. It then provides existing works, history evolution over time, architecture transformers different resources methods that have been used train them. also datasets utilized studies. After that, discusses wide range applications biomedical healthcare, education, social, business, agriculture. illustrates how create society shape future AI they can be solve real-world problems. Then explores open issues challenges deploying scenario. Our aims help practitioners, researchers, experts pre-trained goals.

Language: Английский

Citations

125
Exploring the role of ChatGPT in patient care (diagnosis and treatment) and medical research: A systematic review DOI Creative Commons
Ravindra Kumar Garg, Vijeth L Urs,

Akshay Anand Agrawal

et al.

Health Promotion Perspectives, Journal Year: 2023, Volume and Issue: 13(3), P. 183 - 191

Published: Sept. 11, 2023

Background: ChatGPT is an artificial intelligence based tool developed by OpenAI (California, USA). This systematic review examines the potential of in patient care and its role medical research. Methods: The was done according to PRISMA guidelines. Embase, Scopus, PubMed Google Scholar data bases were searched. We also searched preprint bases. Our search aimed identify all kinds publications, without any restrictions, on application research, publishing care. used term "ChatGPT". reviewed publications including original articles, reviews, editorial/ commentaries, even letter editor. Each selected records analysed using responses generated compiled a table. word table transformed PDF further ChatPDF. Results: full texts 118 articles. can assist with enquiries, note writing, decision-making, trial enrolment, management, decision support, research education. But solutions it offers are usually insufficient contradictory, raising questions about their originality, privacy, correctness, bias, legality. Due lack human-like qualities, ChatGPT’s legitimacy as author questioned when for academic writing. contents have concerns bias possible plagiarism. Conclusion: Although help treatment there issues accuracy, authorship, bias. serve "clinical assistant" be scholarly

Language: Английский

Citations

106
Chatbots and Large Language Models in Radiology: A Practical Primer for Clinical and Research Applications DOI
Rajesh Bhayana

Radiology, Journal Year: 2024, Volume and Issue: 310(1)

Published: Jan. 1, 2024

Although chatbots have existed for decades, the emergence of transformer-based large language models (LLMs) has captivated world through most recent wave artificial intelligence chatbots, including ChatGPT. Transformers are a type neural network architecture that enables better contextual understanding and efficient training on massive amounts unlabeled data, such as unstructured text from internet. As LLMs increased in size, their improved performance emergent abilities revolutionized natural processing. Since is integral to human thought, applications based transformative potential many industries. In fact, LLM-based demonstrated human-level professional benchmarks, radiology. offer numerous clinical research radiology, several which been explored literature with encouraging results. Multimodal can simultaneously interpret images generate reports, closely mimicking current diagnostic pathways Thus, requisition report, opportunity positively impact nearly every step radiology journey. Yet, these impressive not without limitations. This article reviews limitations mitigation strategies, well uses LLMs, multimodal models. Also reviewed existing enhance efficiency supervised settings.

Language: Английский

Citations

105
Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges DOI Creative Commons
Yan Chen, Pouyan Esmaeilzadeh

Journal of Medical Internet Research, Journal Year: 2024, Volume and Issue: 26, P. e53008 - e53008

Published: March 8, 2024

As advances in artificial intelligence (AI) continue to transform and revolutionize the field of medicine, understanding potential uses generative AI health care becomes increasingly important. Generative AI, including models such as adversarial networks large language models, shows promise transforming medical diagnostics, research, treatment planning, patient care. However, these data-intensive systems pose new threats protected information. This Viewpoint paper aims explore various categories care, drug discovery, virtual assistants, clinical decision support, while identifying security privacy within each phase life cycle (ie, data collection, model development, implementation phases). The objectives this study were analyze current state identify opportunities challenges posed by integrating technologies into existing infrastructure, propose strategies for mitigating risks. highlights importance addressing associated with ensure safe effective use systems. findings can inform development future help organizations better understand benefits risks By examining cases across diverse domains contributes theoretical discussions surrounding ethics, vulnerabilities, regulations. In addition, provides practical insights stakeholders looking adopt solutions their organizations.

Language: Английский

Citations

94
Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions DOI Creative Commons
Tugba Akinci D’Antonoli, Arnaldo Stanzione,

Christian Bluethgen

et al.

Diagnostic and Interventional Radiology, Journal Year: 2023, Volume and Issue: 30(2), P. 80 - 90

Published: Oct. 4, 2023

adiology is one of the most technology-driven medical specialties and has always been closely linked to computer science.In particular, ever since picture archiving communication system (PACS) revolution, there have many examples emerging new technology that shaped reshaped day-to-day practice radiologists. 1 More recently, scientific community witnessed remarkable progress artificial intelligence (AI), advances in image-recognition tasks are likely herald another significant leap forward for radiology practice.2There potential applications AI almost entire workflow, such as image quality improvement (e.g., reducing acquisition time and/or radiation dose), post-processing annotation segmentation), interpretation prediction diagnosis).3With advent natural language processing (NLP) especially with development large models (LLMs), it becoming clear not limited imaging-related radiology, LLMs a impact radiologists mainly provide textual reports comprising their interpretations diagnostic images clinical significance.The origins date back 1950s, pivotal decade establishment an academic discipline successful demonstration machine translation through Georgetown-IBM experiment.4Before delving into milestones led today, imperative establish definitions introduce key concepts.In essence, model program designed process human varies size complexity from small rule-based systems sophisticated AI-driven models.On other hand, represent exceptional class distinguished by scale, complexity, emergent capabilities found smaller-scale counterparts.5These models, built on deep learning architectures trained vast data billions parameters, excel diverse range NLP tasks, summarization, translation, sentiment analysis, text generation.Put simply, predict next word or token given sequence words.

Language: Английский

Citations

93
Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicine DOI Creative Commons
Thomas Savage, Ashwin Nayak,

Robert Gallo

et al.

npj Digital Medicine, Journal Year: 2024, Volume and Issue: 7(1)

Published: Jan. 24, 2024

Abstract One of the major barriers to using large language models (LLMs) in medicine is perception they use uninterpretable methods make clinical decisions that are inherently different from cognitive processes clinicians. In this manuscript we develop diagnostic reasoning prompts study whether LLMs can imitate while accurately forming a diagnosis. We find GPT-4 be prompted mimic common clinicians without sacrificing accuracy. This significant because an LLM provide interpretable rationale offers physicians means evaluate response likely correct and trusted for patient care. Prompting have potential mitigate “black box” limitations LLMs, bringing them one step closer safe effective medicine.

Language: Английский

Citations

80
Performance evaluation of ChatGPT, GPT-4, and Bard on the official board examination of the Japan Radiology Society DOI Creative Commons
Yoshitaka Toyama, Ayaka Harigai, Mirei Abe

et al.

Japanese Journal of Radiology, Journal Year: 2023, Volume and Issue: 42(2), P. 201 - 207

Published: Oct. 4, 2023

Abstract Purpose Herein, we assessed the accuracy of large language models (LLMs) in generating responses to questions clinical radiology practice. We compared performance ChatGPT, GPT-4, and Google Bard using from Japan Radiology Board Examination (JRBE). Materials methods In total, 103 JRBE 2022 were used with permission Radiological Society. These categorized by pattern, required level thinking, topic. McNemar’s test was compare proportion correct between LLMs. Fisher’s exact assess GPT-4 for each topic category. Results correctly answered 40.8% (42 103), 65.0% (67 38.8% (40 103) questions, respectively. significantly outperformed ChatGPT 24.2% ( p < 0.001) 26.2% 0.001). categorical analysis 79.7% lower-order which higher than or The question pattern revealed GPT-4’s superiority over (67.4% vs. 46.5%, = 0.004) (39.5%, single-answer questions. that (40%, 0.013) (26.7%, 0.004). No significant differences observed LLMs categories not mentioned above. better nuclear medicine (93.3%) diagnostic (55.8%; also performed on higher-order (79.7% 45.5%, Conclusion ChatGPTplus based scored 65% when answering Japanese JRBE, outperforming Bard. This highlights potential address advanced field Japan.

Language: Английский

Citations

79
Evaluation and mitigation of the limitations of large language models in clinical decision-making DOI Creative Commons
Paul Hager, Friederike Jungmann, Robbie Holland

et al.

Nature Medicine, Journal Year: 2024, Volume and Issue: 30(9), P. 2613 - 2622

Published: July 4, 2024

Clinical decision-making is one of the most impactful parts a physician's responsibilities and stands to benefit greatly from artificial intelligence solutions large language models (LLMs) in particular. However, while LLMs have achieved excellent performance on medical licensing exams, these tests fail assess many skills necessary for deployment realistic clinical environment, including gathering information, adhering guidelines, integrating into workflows. Here we created curated dataset based Medical Information Mart Intensive Care database spanning 2,400 real patient cases four common abdominal pathologies as well framework simulate setting. We show that current state-of-the-art do not accurately diagnose patients across all (performing significantly worse than physicians), follow neither diagnostic nor treatment cannot interpret laboratory results, thus posing serious risk health patients. Furthermore, move beyond accuracy demonstrate they be easily integrated existing workflows because often instructions are sensitive both quantity order information. Overall, our analysis reveals currently ready autonomous providing guide future studies.

Language: Английский

Citations

75