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Kulkarni S. AI and Euro 2024: VAR is shaking up football - and it's not going away. Nature 2024; 630:538-539. [PMID: 38871874 DOI: 10.1038/d41586-024-01764-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
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Tong X, Zhang X, Fensholt R, Jensen PRD, Li S, Larsen MN, Reiner F, Tian F, Brandt M. Global area boom for greenhouse cultivation revealed by satellite mapping. NATURE FOOD 2024; 5:513-523. [PMID: 38741004 DOI: 10.1038/s43016-024-00985-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 04/16/2024] [Indexed: 05/16/2024]
Abstract
Greenhouse cultivation has been expanding rapidly in recent years, yet little knowledge exists on its global extent and expansion. Using commercial and freely available satellite data combined with artificial intelligence techniques, we present a global assessment of greenhouse cultivation coverage and map 1.3 million hectares of greenhouse infrastructures in 2019, a much larger extent than previously estimated. Our analysis includes both large (61%) and small-scale (39%) greenhouse infrastructures. Examining the temporal development of the 65 largest clusters (>1,500 ha), we show a recent upsurge in greenhouse cultivation in the Global South since the 2000s, including a dramatic increase in China, accounting for 60% of the global coverage. We emphasize the potential of greenhouse infrastructures to enhance food security but raise awareness of the uncertain environmental and social implications that may arise from this expansion. We further highlight the gap in spatio-temporal datasets for supporting future research agendas on this critical topic.
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Haverkamp W, Strodthoff N. [Artificial intelligence-enhanced electrocardiography : Will it revolutionize diagnosis and management of our patients?]. Herzschrittmacherther Elektrophysiol 2024; 35:104-110. [PMID: 38361131 DOI: 10.1007/s00399-024-00997-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 01/23/2024] [Indexed: 02/17/2024]
Abstract
The use of artificial intelligence (AI) in healthcare has made significant progress in the last 10 years. Many experts believe that utilization of AI technologies, especially deep learning, will bring about drastic changes in how physicians understand, diagnose, and treat diseases. One aspect of this development is AI-enhanced electrocardiography (ECG) analysis. It involves not only optimizing the traditional ECG analysis by the physician and improving the accuracy of automatic interpretation by the ECG device but also introducing entirely new diagnostic options enabled by AI. Examples include assessing left ventricular function, predicting atrial fibrillation, and diagnosing both cardiac and noncardiac conditions. Through AI, the ECG becomes a comprehensive tool for screening, diagnosis, and patient management, potentially revolutionizing clinical practices. This paper provides an overview of the current state of this development, discusses existing limitations, and explores the challenges that may arise for healthcare professionals in this context.
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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. CURRENTS IN PHARMACY TEACHING & LEARNING 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] [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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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] [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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Meta AI system is a boost to endangered languages - as long as humans aren't forgotten. Nature 2024; 630:8. [PMID: 38840018 DOI: 10.1038/d41586-024-01619-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
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Podichetty JT, Sardar S, Henscheid N, Lee GV, Abrams JR, Anderson W, Ma SC, Romero K. Accelerating healthcare innovation: the role of Artificial intelligence and digital health technologies in critical path institute's public-private partnerships. Clin Transl Sci 2024; 17:e13851. [PMID: 38807460 PMCID: PMC11133960 DOI: 10.1111/cts.13851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 05/10/2024] [Accepted: 05/14/2024] [Indexed: 05/30/2024] Open
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Callaway E. Who will make AlphaFold3 open source? Scientists race to crack AI model. Nature 2024; 630:14-15. [PMID: 38783131 DOI: 10.1038/d41586-024-01555-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
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Grewal JS, Sengupta PP. Pitfalls and Opportunities for the Growing Role of AI in Heart Failure. J Card Fail 2024; 30:838-840. [PMID: 38479576 DOI: 10.1016/j.cardfail.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 03/04/2024] [Indexed: 04/17/2024]
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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] [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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Blanch L, Santos-Pulpón V, Roca O, Sarlabous L, de Haro C. Artificial intelligence as a further step in the detection of dyspnea in the critically ill mechanically ventilated patient. Intensive Care Med 2024; 50:1015-1016. [PMID: 38695926 DOI: 10.1007/s00134-024-07420-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/26/2024] [Indexed: 06/11/2024]
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Wang D. How I'm using AI tools to help universities maximize research impacts. Nature 2024; 630:794. [PMID: 38926626 DOI: 10.1038/d41586-024-02081-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
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Han K, Liu C, Friedman D. Artificial intelligence/machine learning for epilepsy and seizure diagnosis. Epilepsy Behav 2024; 155:109736. [PMID: 38636146 DOI: 10.1016/j.yebeh.2024.109736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 03/03/2024] [Accepted: 03/10/2024] [Indexed: 04/20/2024]
Abstract
Accurate seizure and epilepsy diagnosis remains a challenging task due to the complexity and variability of manifestations, which can lead to delayed or missed diagnosis. Machine learning (ML) and artificial intelligence (AI) is a rapidly developing field, with growing interest in integrating and applying these tools to aid clinicians facing diagnostic uncertainties. ML algorithms, particularly deep neural networks, are increasingly employed in interpreting electroencephalograms (EEG), neuroimaging, wearable data, and seizure videos. This review discusses the development and testing phases of AI/ML tools, emphasizing the importance of generalizability and interpretability in medical applications, and highlights recent publications that demonstrate the current and potential utility of AI to aid clinicians in diagnosing epilepsy. Current barriers of AI integration in patient care include dataset availability and heterogeneity, which limit studies' quality, interpretability, comparability, and generalizability. ML and AI offer substantial promise in improving the accuracy and efficiency of epilepsy diagnosis. The growing availability of diverse datasets, enhanced processing speed, and ongoing efforts to standardize reporting contribute to the evolving landscape of AI applications in clinical care.
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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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 02/16/2024]
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Fischer L. Applying Artificial Intelligence to Perioperative Nursing Practice. AORN J 2024; 119:P1-P4. [PMID: 38804724 DOI: 10.1002/aorn.14156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 04/04/2024] [Indexed: 05/29/2024]
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Adelani DI. Meta's AI translation model embraces overlooked languages. Nature 2024; 630:821-822. [PMID: 38839996 DOI: 10.1038/d41586-024-00964-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
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Human neuroscience is entering a new era - it mustn't forget its human dimension. Nature 2024; 630:530. [PMID: 38898298 DOI: 10.1038/d41586-024-02022-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
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Goldenholz DM, Eccleston C, Moss R, Westover MB. Prospective validation of a seizure diary forecasting falls short. Epilepsia 2024; 65:1730-1736. [PMID: 38606580 PMCID: PMC11166505 DOI: 10.1111/epi.17984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 03/13/2024] [Accepted: 04/02/2024] [Indexed: 04/13/2024]
Abstract
OBJECTIVE Recently, a deep learning artificial intelligence (AI) model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm. METHODS We recruited a prospective cohort of 46 people with epilepsy; 25 completed sufficient data entry for analysis (median = 5 months). We used the same AI method as in our prior study. Group-level and individual-level Brier Skill Scores (BSSs) compared random forecasts and simple moving average forecasts to the AI. RESULTS The AI had an area under the receiver operating characteristic curve of .82. At the group level, the AI outperformed random forecasting (BSS = .53). At the individual level, AI outperformed random in 28% of cases. At the group and individual level, the moving average outperformed the AI. If pre-enrollment (nonverified) diaries (with presumed underreporting) were included, the AI significantly outperformed both comparators. Surveys showed most did not mind poor-quality LOW-RISK or HIGH-RISK forecasts, yet 91% wanted access to these forecasts. SIGNIFICANCE The previously developed AI forecasting tool did not outperform a very simple moving average forecasting in this prospective cohort, suggesting that the AI model should be replaced.
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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] [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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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] [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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van Genderen ME, Cecconi M, Jung C. Federated data access and federated learning: improved data sharing, AI model development, and learning in intensive care. Intensive Care Med 2024; 50:974-977. [PMID: 38635044 PMCID: PMC11164808 DOI: 10.1007/s00134-024-07408-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 03/23/2024] [Indexed: 04/19/2024]
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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? JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 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] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 11/15/2023] [Indexed: 11/20/2023]
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Gibney E. The AI revolution is coming to robots: how will it change them? Nature 2024; 630:22-24. [PMID: 38822186 DOI: 10.1038/d41586-024-01442-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2024]
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Garisto D. How cutting-edge computer chips are speeding up the AI revolution. Nature 2024; 630:544-546. [PMID: 38834691 DOI: 10.1038/d41586-024-01544-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
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Abbasi J, Hswen Y. One Day, AI Could Mean Better Mental Health for All. JAMA 2024; 331:1691-1694. [PMID: 38700871 DOI: 10.1001/jama.2023.27727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
This Medical News article is an interview with psychiatrist Vikram Patel, chair of the Department of Global Health and Social Medicine at Harvard Medical School.
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