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Kusunose K. New era in right ventricular imaging with AI: Challenges and horizons. Int J Cardiol 2024; 406:132002. [PMID: 38575000 DOI: 10.1016/j.ijcard.2024.132002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 03/29/2024] [Indexed: 04/06/2024]
Affiliation(s)
- Kenya Kusunose
- Department of Cardiovascular Medicine, Nephrology, and Neurology, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japan.
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2
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Overkamp F. [A look into the neighboring discipline: eHealth in oncology]. Chirurgie (Heidelb) 2024; 95:451-458. [PMID: 38727743 DOI: 10.1007/s00104-024-02089-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/09/2024] [Indexed: 05/16/2024]
Abstract
Digitalization is dramatically changing the entire healthcare system. Keywords such as artificial intelligence, electronic patient files (ePA), electronic prescriptions (eRp), telemedicine, wearables, augmented reality and digital health applications (DiGA) represent the digital transformation that is already taking place. Digital becomes real! This article outlines the state of research and development, current plans and ongoing uses of digital tools in oncology in the first half of 2024. The possibilities for using artificial intelligence and the use of DiGAs in oncology are presented in more detail in this overview according to their stage of development as they already show a noticeable benefit in oncology.
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Affiliation(s)
- Friedrich Overkamp
- OncoConsult Overkamp GmbH, Europaplatz 2, 10557, Berlin, Deutschland.
- onkowissen.de GmbH, Würzburg, Deutschland.
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3
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Taesotikul S, Singhan W, Taesotikul T. ChatGPT vs pharmacy students in the pharmacotherapy time-limit test: A comparative study in Thailand. Curr Pharm Teach Learn 2024; 16:404-410. [PMID: 38641483 DOI: 10.1016/j.cptl.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 04/21/2024]
Abstract
OBJECTIVES ChatGPT is an innovative artificial intelligence designed to enhance human activities and serve as a potent tool for information retrieval. This study aimed to evaluate the performance and limitation of ChatGPT on fourth-year pharmacy student examination. METHODS This cross-sectional study was conducted on February 2023 at the Faculty of Pharmacy, Chiang Mai University, Thailand. The exam contained 16 multiple-choice questions and 2 short-answer questions, focusing on classification and medical management of shock and electrolyte disorders. RESULTS Out of the 18 questions, ChatGPT provided 44% (8 out of 18) correct responses. In contrast, the students provided a higher accuracy rate with 66% (12 out of 18) correctly answered questions. The findings of this study underscore that while AI exhibits proficiency, it encounters limitations when confronted with specific queries derived from practical scenarios, on the contrary with pharmacy students who possess the liberty to explore and collaborate, mirroring real-world scenarios. CONCLUSIONS Users must exercise caution regarding its reliability, and interpretations of AI-generated answers should be approached judiciously due to potential restrictions in multi-step analysis and reliance on outdated data. Future advancements in AI models, with refinements and tailored enhancements, offer the potential for improved performance.
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Affiliation(s)
- Suthinee Taesotikul
- Department of Pharmaceutical Care, Faculty of Pharmacy, Chiang Mai University, Chiang Mai 50200, Thailand.
| | - Wanchana Singhan
- Department of Pharmaceutical Care, Faculty of Pharmacy, Chiang Mai University, Chiang Mai 50200, Thailand.
| | - Theerada Taesotikul
- Department of Biomedicine and Health Informatics, Faculty of Pharmacy, Silpakorn University, Nakhon Pathom 73000, Thailand.
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4
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Burns JK, Etherington C, Cheng-Boivin O, Boet S. Using an artificial intelligence tool can be as accurate as human assessors in level one screening for a systematic review. Health Info Libr J 2024; 41:136-148. [PMID: 34792285 DOI: 10.1111/hir.12413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 08/19/2021] [Accepted: 10/23/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND Artificial intelligence (AI) offers a promising solution to expedite various phases of the systematic review process such as screening. OBJECTIVE We aimed to assess the accuracy of an AI tool in identifying eligible references for a systematic review compared to identification by human assessors. METHODS For the case study (a systematic review of knowledge translation interventions), we used a diagnostic accuracy design and independently assessed for eligibility a set of articles (n = 300) using human raters and the AI system DistillerAI (Evidence Partners, Ottawa, Canada). We analysed a series of 64 possible confidence levels for the AI's decisions and calculated several standard parameters of diagnostic accuracy for each. RESULTS When set to a lower AI confidence threshold of 0.1 or greater and an upper threshold of 0.9 or lower, DistillerAI made article selection decisions very similarly to human assessors. Within this range, DistillerAI made a decision on the majority of articles (93-100%), with a sensitivity of 1.0 and specificity ranging from 0.9 to 1.0. CONCLUSION DistillerAI appears to be accurate in its assessment of articles in a case study of 300 articles. Further experimentation with DistillerAI will establish its performance among other subject areas.
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Affiliation(s)
- Joseph K Burns
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Cole Etherington
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Olivia Cheng-Boivin
- Department of Anesthesiology and Pain Medicine, The Ottawa Hospital, Ottawa, ON, Canada
| | - Sylvain Boet
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
- Department of Anesthesiology and Pain Medicine, The Ottawa Hospital, Ottawa, ON, Canada
- Francophone Affairs & Department of Innovation in Medical Education, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Ottawa Hospital Research Institute, Ottawa, ON, Canada
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Dundaru-Bandi D, Antel R, Ingelmo P. Advances in pediatric perioperative care using artificial intelligence. Curr Opin Anaesthesiol 2024; 37:251-258. [PMID: 38441085 DOI: 10.1097/aco.0000000000001368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
PURPOSE OF THIS REVIEW This article explores how artificial intelligence (AI) can be used to evaluate risks in pediatric perioperative care. It will also describe potential future applications of AI, such as models for airway device selection, controlling anesthetic depth and nociception during surgery, and contributing to the training of pediatric anesthesia providers. RECENT FINDINGS The use of AI in healthcare has increased in recent years, largely due to the accessibility of large datasets, such as those gathered from electronic health records. Although there has been less focus on pediatric anesthesia compared to adult anesthesia, research is on- going, especially for applications focused on risk factor identification for adverse perioperative events. Despite these advances, the lack of formal external validation or feasibility testing results in uncertainty surrounding the clinical applicability of these tools. SUMMARY The goal of using AI in pediatric anesthesia is to assist clinicians in providing safe and efficient care. Given that children are a vulnerable population, it is crucial to ensure that both clinicians and families have confidence in the clinical tools used to inform medical decision- making. While not yet a reality, the eventual incorporation of AI-based tools holds great potential to contribute to the safe and efficient care of our patients.
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Affiliation(s)
| | - Ryan Antel
- Department of Anesthesia, McGill University
| | - Pablo Ingelmo
- Department of Anesthesia, McGill University
- Division of Pediatric Anesthesia
- Edwards Family Interdisciplinary Center for Complex Pain. Montreal Children's Hospital
- Research Institute, McGill University Health Center
- Alan Edwards for Research on Pain. McGill University, Montreal, Quebec, Canada
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6
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Mazhar MA, Qazi S, Sarwat S. The future of anatomy education: Simulation-based and AI-based learning. J Clin Nurs 2024; 33:2357-2358. [PMID: 38356203 DOI: 10.1111/jocn.17074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 02/16/2024]
Affiliation(s)
- Muhammad Atif Mazhar
- Department of Anatomy, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Sadia Qazi
- Department of Anatomy, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Surriyya Sarwat
- Liaquat International Medical and Technical College, Liaquat Institute of Medical and Health Sciences, LUMHS, Thatta, Pakistan
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Biesheuvel LA, Dongelmans DA, Elbers PW. Artificial intelligence to advance acute and intensive care medicine. Curr Opin Crit Care 2024; 30:246-250. [PMID: 38525882 PMCID: PMC11064910 DOI: 10.1097/mcc.0000000000001150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
PURPOSE OF REVIEW This review explores recent key advancements in artificial intelligence for acute and intensive care medicine. As artificial intelligence rapidly evolves, this review aims to elucidate its current applications, future possibilities, and the vital challenges that are associated with its integration into emergency medical dispatch, triage, medical consultation and ICUs. RECENT FINDINGS The integration of artificial intelligence in emergency medical dispatch (EMD) facilitates swift and accurate assessment. In the emergency department (ED), artificial intelligence driven triage models leverage diverse patient data for improved outcome predictions, surpassing human performance in retrospective studies. Artificial intelligence can streamline medical documentation in the ED and enhances medical imaging interpretation. The introduction of large multimodal generative models showcases the future potential to process varied biomedical data for comprehensive decision support. In the ICU, artificial intelligence applications range from early warning systems to treatment suggestions. SUMMARY Despite promising academic strides, widespread artificial intelligence adoption in acute and critical care is hindered by ethical, legal, technical, organizational, and validation challenges. Despite these obstacles, artificial intelligence's potential to streamline clinical workflows is evident. When these barriers are overcome, future advancements in artificial intelligence have the potential to transform the landscape of patient care for acute and intensive care medicine.
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Affiliation(s)
- Laurens A. Biesheuvel
- Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence (C4I), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam Public Health (APH), Amsterdam UMC
- Quantitative Data Analytics Group, Department of Computer Science, Faculty of Science, Vrije Universiteit
| | - Dave A. Dongelmans
- Department of Intensive Care Medicine, Amsterdam Public Health (APH), Amsterdam UMC, University of Amsterdam
- National Intensive Care Evaluation Foundation, Amsterdam, The Netherlands
| | - Paul W.G. Elbers
- Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence (C4I), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam Public Health (APH), Amsterdam UMC
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Pandi-Perumal SR, Narasimhan M, Seeman MV, Jahrami H. Artificial intelligence is set to transform mental health services. CNS Spectr 2024; 29:155-157. [PMID: 37706366 DOI: 10.1017/s1092852923002456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
The current development in the field of artificial intelligence and its applications has advantages and disadvantages in the digital age that we now live in. The state of the use of AI for mental health has to be assessed by stakeholders, which includes all of us. We must comprehend the trends, gaps, opportunities, challenges, and shortcomings of this new technology. As the field evolves, rules, regulatory frameworks, guidelines, standards, and policies will develop and will progressively scale upwards. To advance the field, mental health professionals must be prepared to meet obstacles and seize possibilities presented by creative and disruptive technologies like AI. Therefore, a collaborative strategy must include multi-stakeholder participation in basic, translational, and clinical aspects of AI. Mental health practitioners need to be ready to face challenges and embrace and harness the power of innovative and disruptive technology such as AI that could offer to move the field forward.
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Affiliation(s)
- Seithikurippu R Pandi-Perumal
- Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Tamil Nadu, India
- Division of Research and Development, Lovely Professional University, Phagwara, India
| | - Meera Narasimhan
- Department of Neuropsychiatry and Behavioral Science, School of Medicine, University of South Carolina, Columbia, SC, USA
| | - Mary V Seeman
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Haitham Jahrami
- Government Hospitals, Manama, Bahrain
- Department of Psychiatry, College of Medicine and Medical Sciences, Arabian Gulf University, Manama, Bahrain
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de Araújo Lopes NV, Nonaka CFW, Alves PM, Cunha JLS. Will artificial intelligence chatbots revolutionize the way patients with oral diseases access information? J Stomatol Oral Maxillofac Surg 2024; 125:101703. [PMID: 37979783 DOI: 10.1016/j.jormas.2023.101703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 11/15/2023] [Indexed: 11/20/2023]
Affiliation(s)
- Natália Vitória de Araújo Lopes
- Postgraduate Program in Dentistry, Department of Dentistry, State University of Paraíba (UEPB), Rua das Baraúnas, 351 - Bairro Universitário, Campina Grande, PB 58429-500, Brazil
| | - Cassiano Francisco Weege Nonaka
- Postgraduate Program in Dentistry, Department of Dentistry, State University of Paraíba (UEPB), Rua das Baraúnas, 351 - Bairro Universitário, Campina Grande, PB 58429-500, Brazil
| | - Pollianna Muniz Alves
- Postgraduate Program in Dentistry, Department of Dentistry, State University of Paraíba (UEPB), Rua das Baraúnas, 351 - Bairro Universitário, Campina Grande, PB 58429-500, Brazil
| | - John Lennon Silva Cunha
- Postgraduate Program in Dentistry, Department of Dentistry, State University of Paraíba (UEPB), Rua das Baraúnas, 351 - Bairro Universitário, Campina Grande, PB 58429-500, Brazil.
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10
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Spirnak JR, Antani S. The Need for Artificial Intelligence Curriculum in Military Medical Education. Mil Med 2024; 189:954-958. [PMID: 37864817 DOI: 10.1093/milmed/usad412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 09/25/2023] [Accepted: 10/04/2023] [Indexed: 10/23/2023] Open
Abstract
The success of deep-learning algorithms in analyzing complex structured and unstructured multidimensional data has caused an exponential increase in the amount of research devoted to the applications of artificial intelligence (AI) in medicine in the past decade. Public release of large language models like ChatGPT the past year has generated an unprecedented storm of excitement and rumors of machine intelligence finally reaching or even surpassing human capability in detecting meaningful signals in complex multivariate data. Such enthusiasm, however, is met with an equal degree of both skepticism and fear over the social, legal, and moral implications of such powerful technology with relatively little safeguards or regulations on its development. The question remains in medicine of how to harness the power of AI to improve patient outcomes by increasing the diagnostic accuracy and treatment precision provided by medical professionals. Military medicine, given its unique mission and resource constraints,can benefit immensely from such technology. However, reaping such benefits hinges on the ability of the rising generations of military medical professionals to understand AI algorithms and their applications. Additionally, they should strongly consider working with them as an adjunct decision-maker and view them as a colleague to access and harness relevant information as opposed to something to be feared. Ideas expressed in this commentary were formulated by a military medical student during a two-month research elective working on a multidisciplinary team of computer scientists and clinicians at the National Library of Medicine advancing the state of the art of AI in medicine. A motivation to incorporate AI in the Military Health System is provided, including examples of applications in military medicine. Rationale is then given for inclusion of AI in education starting in medical school as well as a prudent implementation of these algorithms in a clinical workflow during graduate medical education. Finally, barriers to implementation are addressed along with potential solutions. The end state is not that rising military physicians are technical experts in AI; but rather that they understand how they can leverage its rapidly evolving capabilities to prepare for a future where AI will have a significant role in clinical care. The overall goal is to develop trained clinicians that can leverage these technologies to improve the Military Health System.
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Affiliation(s)
- Jonathan R Spirnak
- Uniformed Services University of the Health Sciences (USUHS) School of Medicine, Bethesda, MD 20814, USA
| | - Sameer Antani
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
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Meşe İ, Altıntaş Taşlıçay C, Kuzan BN, Kuzan TY, Sivrioğlu AK. Educating the next generation of radiologists: a comparative report of ChatGPT and e-learning resources. Diagn Interv Radiol 2024; 30:163-174. [PMID: 38145370 PMCID: PMC11095068 DOI: 10.4274/dir.2023.232496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 11/29/2023] [Indexed: 12/26/2023]
Abstract
Rapid technological advances have transformed medical education, particularly in radiology, which depends on advanced imaging and visual data. Traditional electronic learning (e-learning) platforms have long served as a cornerstone in radiology education, offering rich visual content, interactive sessions, and peer-reviewed materials. They excel in teaching intricate concepts and techniques that necessitate visual aids, such as image interpretation and procedural demonstrations. However, Chat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence (AI)-powered language model, has made its mark in radiology education. It can generate learning assessments, create lesson plans, act as a round-the-clock virtual tutor, enhance critical thinking, translate materials for broader accessibility, summarize vast amounts of information, and provide real-time feedback for any subject, including radiology. Concerns have arisen regarding ChatGPT's data accuracy, currency, and potential biases, especially in specialized fields such as radiology. However, the quality, accessibility, and currency of e-learning content can also be imperfect. To enhance the educational journey for radiology residents, the integration of ChatGPT with expert-curated e-learning resources is imperative for ensuring accuracy and reliability and addressing ethical concerns. While AI is unlikely to entirely supplant traditional radiology study methods, the synergistic combination of AI with traditional e-learning can create a holistic educational experience.
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Affiliation(s)
- İsmail Meşe
- University of Health Sciences Türkiye, Erenköy Mental Health and Neurology Training and Research Hospital, Clinic of Radiology, İstanbul, Türkiye
| | | | - Beyza Nur Kuzan
- Kartal Dr. Lütfi Kırdar City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Taha Yusuf Kuzan
- Sancaktepe Şehit Prof. Dr. İlhan Varank Training and Research Hospital, Clinic of Radiology, İstanbul, Türkiye
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Cheng K, Sun Z, Liu X, Wu H, Li C. Generative artificial intelligence is infiltrating peer review process. Crit Care 2024; 28:149. [PMID: 38715069 PMCID: PMC11077838 DOI: 10.1186/s13054-024-04933-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Accepted: 04/27/2024] [Indexed: 05/12/2024] Open
Affiliation(s)
- Kunming Cheng
- Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zaijie Sun
- Department of Orthopaedics, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Xiaojun Liu
- Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Haiyang Wu
- Department of Orthopaedics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
- Department of Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China.
| | - Cheng Li
- Department of Orthopaedic Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
- Center for Musculoskeletal Surgery (CMSC), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt University of Berlin, Berlin, Germany.
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13
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Zhu L, Mou W, Hong C, Yang T, Lai Y, Qi C, Lin A, Zhang J, Luo P. The Evaluation of Generative AI Should Include Repetition to Assess Stability. JMIR Mhealth Uhealth 2024; 12:e57978. [PMID: 38688841 PMCID: PMC11106698 DOI: 10.2196/57978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/30/2024] [Indexed: 05/02/2024] Open
Abstract
The increasing interest in the potential applications of generative artificial intelligence (AI) models like ChatGPT in health care has prompted numerous studies to explore its performance in various medical contexts. However, evaluating ChatGPT poses unique challenges due to the inherent randomness in its responses. Unlike traditional AI models, ChatGPT generates different responses for the same input, making it imperative to assess its stability through repetition. This commentary highlights the importance of including repetition in the evaluation of ChatGPT to ensure the reliability of conclusions drawn from its performance. Similar to biological experiments, which often require multiple repetitions for validity, we argue that assessing generative AI models like ChatGPT demands a similar approach. Failure to acknowledge the impact of repetition can lead to biased conclusions and undermine the credibility of research findings. We urge researchers to incorporate appropriate repetition in their studies from the outset and transparently report their methods to enhance the robustness and reproducibility of findings in this rapidly evolving field.
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Affiliation(s)
- Lingxuan Zhu
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Weiming Mou
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chenglin Hong
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Tao Yang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yancheng Lai
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Chang Qi
- Institute of Logic and Computation, TU Wien, Austria
| | - Anqi Lin
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jian Zhang
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Peng Luo
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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14
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Usta U, Taştekin E. Present and Future of Artificial Intelligence in Pathology. Balkan Med J 2024; 41:157-158. [PMID: 38700263 PMCID: PMC11077921 DOI: 10.4274/balkanmedj.galenos.2024.2024.060324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2024] Open
Affiliation(s)
- Ufuk Usta
- Department of Pathology, Trakya University Faculty of Medicine, Edirne, Türkiye
| | - Ebru Taştekin
- Department of Pathology, Trakya University Faculty of Medicine, Edirne, Türkiye
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15
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Adam D. Lethal AI weapons are here: how can we control them? Nature 2024; 629:521-523. [PMID: 38653827 DOI: 10.1038/d41586-024-01029-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
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16
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Anderson B. How to bridge innovation and regulation for responsible AI in healthcare. Nat Med 2024; 30:1231. [PMID: 38760588 DOI: 10.1038/s41591-024-02983-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2024]
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17
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Haykal D, Garibyan L, Flament F, Cartier H. Hybrid cosmetic dermatology: AI generated horizon. Skin Res Technol 2024; 30:e13721. [PMID: 38696225 PMCID: PMC11064925 DOI: 10.1111/srt.13721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 04/15/2024] [Indexed: 05/04/2024]
Affiliation(s)
| | - Lilit Garibyan
- Wellman Center for PhotomedicineMassachusetts General HospitalBostonMassachusettsUSA
- Department of DermatologyHarvard Medical SchoolBostonMassachusettsUSA
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18
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Fink T. Why mathematics is set to be revolutionized by AI. Nature 2024; 629:505. [PMID: 38745094 DOI: 10.1038/d41586-024-01413-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
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Maw AM, Trinkley KE, Glasgow RE. The Role of Pragmatic Implementation Science Methods in Achieving Equitable and Effective Use of Artificial Intelligence in Healthcare. J Gen Intern Med 2024; 39:1242-1244. [PMID: 38172408 PMCID: PMC11116336 DOI: 10.1007/s11606-023-08580-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024]
Affiliation(s)
- Anna M Maw
- Adult and Child Center for Outcomes Research and Delivery Science Center, Aurora, CO, USA.
- Division of Hospital Medicine, Department of Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, 12605 E 16th Ave, Aurora, CO, 80045, USA.
| | - Katy E Trinkley
- Adult and Child Center for Outcomes Research and Delivery Science Center, Aurora, CO, USA
- Department of Family Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Russell E Glasgow
- Division of Hospital Medicine, Department of Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, 12605 E 16th Ave, Aurora, CO, 80045, USA
- Department of Family Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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20
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Gilbert N. NATO is boosting AI and climate research as scientific diplomacy remains on ice. Nature 2024; 629:18-19. [PMID: 38664554 DOI: 10.1038/d41586-024-01052-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
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21
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Kiefer B. IA, prédation, jungle et clowns. Rev Med Suisse 2024; 20:904. [PMID: 38693808 DOI: 10.53738/revmed.2024.20.872.904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
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22
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Callaway E. Major AlphaFold upgrade offers boost for drug discovery. Nature 2024; 629:509-510. [PMID: 38719965 DOI: 10.1038/d41586-024-01383-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
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23
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Arnaout R. Adapting vision-language AI models to cardiology tasks. Nat Med 2024; 30:1245-1246. [PMID: 38693248 DOI: 10.1038/s41591-024-02956-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Affiliation(s)
- Rima Arnaout
- Department of Medicine, Division of Cardiology, Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- UCSF-UC Berkeley Joint Program in Computational Precision Health, Department of Radiology, Center for Intelligent Imaging, University of California, San Francisco, San Francisco, CA, USA.
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24
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Larsen PD. Artificial Intelligence and Nursing Practice. Rehabil Nurs 2024; 49:73-74. [PMID: 38696432 DOI: 10.1097/rnj.0000000000000454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2024]
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25
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Prainsack B, Forgó N. New AI regulation in the EU seeks to reduce risk without assessing public benefit. Nat Med 2024; 30:1235-1237. [PMID: 38499661 DOI: 10.1038/s41591-024-02874-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Affiliation(s)
- Barbara Prainsack
- Research Platform Governance of Digital Practices, University of Vienna, Vienna, Austria.
- Department of Political Science, University of Vienna, Vienna, Austria.
- Institute of Advanced Study, Berlin, Germany.
| | - Nikolaus Forgó
- Research Platform Governance of Digital Practices, University of Vienna, Vienna, Austria
- Department of Innovation and Digitalisation in Law, University of Vienna, Vienna, Austria
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26
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Khullar D, Wang X, Wang F. Large Language Models in Health Care: Charting a Path Toward Accurate, Explainable, and Secure AI. J Gen Intern Med 2024; 39:1239-1241. [PMID: 38308153 PMCID: PMC11116282 DOI: 10.1007/s11606-024-08657-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 01/24/2024] [Indexed: 02/04/2024]
Affiliation(s)
- Dhruv Khullar
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
| | - Xingbo Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
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27
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Lam K, Qiu J. Foundation models: the future of surgical artificial intelligence? Br J Surg 2024; 111:znae090. [PMID: 38650580 DOI: 10.1093/bjs/znae090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/29/2024] [Accepted: 03/10/2024] [Indexed: 04/25/2024]
Affiliation(s)
- Kyle Lam
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Jianing Qiu
- Department of Biomedical Engineering, Chinese University of Hong Kong, Hong Kong SAR
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Moingeon P, Garbay C, Dahan M, Fermont I, Benmakhlouf A, Gouyette A, Poitou P, Saint-Pierre A. [The revolution of AI in drug development]. Med Sci (Paris) 2024; 40:369-376. [PMID: 38651962 DOI: 10.1051/medsci/2024028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024] Open
Abstract
Artificial intelligence and machine learning enable the construction of predictive models, which are currently used to assist in decision-making throughout the process of drug discovery and development. These computational models can be used to represent the heterogeneity of a disease, identify therapeutic targets, design and optimize drug candidates, and evaluate the efficacy of these drugs on virtual patients or digital twins. By combining detailed patient characteristics with the prediction of potential drug-candidate properties, artificial intelligence promotes the emergence of a "computational" precision medicine, allowing for more personalized treatments, better tailored to patient specificities with the aid of such predictive models. Based on such new capabilities, a mixed reality approach to the development of new drugs is being adopted by the pharmaceutical industry, which integrates the outputs of predictive virtual models with real-world empirical studies.
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29
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Heidt A. 'Without these tools, I'd be lost': how generative AI aids in accessibility. Nature 2024; 628:462-463. [PMID: 38589449 DOI: 10.1038/d41586-024-01003-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
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Woźniak M, Ksieniewicz P. How to break big tech's stranglehold on AI in academia. Nature 2024; 628:268. [PMID: 38594396 DOI: 10.1038/d41586-024-01039-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
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31
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Wells S. Ready or not, AI is coming to science education - and students have opinions. Nature 2024; 628:459-461. [PMID: 38589448 DOI: 10.1038/d41586-024-01002-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
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Turon G, Njoroge M, Mulubwa M, Duran-Frigola M, Chibale K. AI can help to tailor drugs for Africa - but Africans should lead the way. Nature 2024; 628:265-267. [PMID: 38594395 DOI: 10.1038/d41586-024-01001-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
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Luers A, Koomey J, Masanet E, Gaffney O, Creutzig F, Lavista Ferres J, Horvitz E. Will AI accelerate or delay the race to net-zero emissions? Nature 2024; 628:718-720. [PMID: 38649764 DOI: 10.1038/d41586-024-01137-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
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34
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Chowdhury R. AI-fuelled election campaigns are here - where are the rules? Nature 2024; 628:237. [PMID: 38594400 DOI: 10.1038/d41586-024-00995-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
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35
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Jones N. AI now beats humans at basic tasks - new benchmarks are needed, says major report. Nature 2024; 628:700-701. [PMID: 38622298 DOI: 10.1038/d41586-024-01087-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
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Coombs A, Ong S. Four change-makers seek impact in medical research. Nature 2024; 627:S8-S10. [PMID: 38480969 DOI: 10.1038/d41586-024-00754-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
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37
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O'Callaghan J. How OpenAI's text-to-video tool Sora could change science - and society. Nature 2024; 627:475-476. [PMID: 38472485 DOI: 10.1038/d41586-024-00661-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
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38
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Ananya. AI image generators often give racist and sexist results: can they be fixed? Nature 2024; 627:722-725. [PMID: 38503880 DOI: 10.1038/d41586-024-00674-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
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Gibney E. Chatbot AI makes racist judgements on the basis of dialect. Nature 2024; 627:476-477. [PMID: 38480953 DOI: 10.1038/d41586-024-00779-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
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Abstract
Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists' visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community's ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.
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Affiliation(s)
- Lisa Messeri
- Department of Anthropology, Yale University, New Haven, CT, USA.
| | - M J Crockett
- Department of Psychology, Princeton University, Princeton, NJ, USA.
- University Center for Human Values, Princeton University, Princeton, NJ, USA.
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Why scientists trust AI too much - and what to do about it. Nature 2024; 627:243. [PMID: 38448525 DOI: 10.1038/d41586-024-00639-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
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42
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King RD, Scassa T, Kramer S, Kitano H. Stockholm declaration on AI ethics: why others should sign. Nature 2024; 626:716. [PMID: 38378827 DOI: 10.1038/d41586-024-00517-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
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43
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Callaway E. AlphaFold found thousands of possible psychedelics. Will its predictions help drug discovery? Nature 2024; 626:14-15. [PMID: 38238624 DOI: 10.1038/d41586-024-00130-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2024]
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Marchant J. First passages of rolled-up Herculaneum scroll revealed. Nature 2024; 626:461-462. [PMID: 38316998 DOI: 10.1038/d41586-024-00346-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
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45
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Jones N. How journals are fighting back against a wave of questionable images. Nature 2024; 626:697-698. [PMID: 38347210 DOI: 10.1038/d41586-024-00372-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
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Wachter RM, Brynjolfsson E. Will Generative Artificial Intelligence Deliver on Its Promise in Health Care? JAMA 2024; 331:65-69. [PMID: 38032660 DOI: 10.1001/jama.2023.25054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
Importance Since the introduction of ChatGPT in late 2022, generative artificial intelligence (genAI) has elicited enormous enthusiasm and serious concerns. Observations History has shown that general purpose technologies often fail to deliver their promised benefits for many years ("the productivity paradox of information technology"). Health care has several attributes that make the successful deployment of new technologies even more difficult than in other industries; these have challenged prior efforts to implement AI and electronic health records. However, genAI has unique properties that may shorten the usual lag between implementation and productivity and/or quality gains in health care. Moreover, the health care ecosystem has evolved to make it more receptive to genAI, and many health care organizations are poised to implement the complementary innovations in culture, leadership, workforce, and workflow often needed for digital innovations to flourish. Conclusions and Relevance The ability of genAI to rapidly improve and the capacity of organizations to implement complementary innovations that allow IT tools to reach their potential are more advanced than in the past; thus, genAI is capable of delivering meaningful improvements in health care more rapidly than was the case with previous technologies.
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Affiliation(s)
| | - Erik Brynjolfsson
- Digital Economy Lab and Institute for Human-Centered AI, Stanford University, Palo Alto, California
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47
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Computers make mistakes and AI will make things worse - the law must recognize that. Nature 2024; 625:631. [PMID: 38263299 DOI: 10.1038/d41586-024-00168-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
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48
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Naddaf M. The science events to watch for in 2024. Nature 2024; 625:221-223. [PMID: 38110632 DOI: 10.1038/d41586-023-04044-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
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49
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From Einstein to AI: how 100 years have shaped science. Nature 2023; 624:474. [PMID: 38114684 DOI: 10.1038/d41586-023-04021-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
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