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arXiv 2024年

作者： Graham, Benjamin A.T. Brown, Lauren Chochlakis, Georgios Dehghani, Morteza Delerme, Raquel Friedman, Brittany Graeden, Ellie Golazizian, Preni Hebbar, Rajat Hejabi, Parsa Kommineni, Aditya Salinas, Mayagüez Sierra-Arévalo, Michael Trager, Jackson Weller, Nicholas Narayanan, Shrikanth Department of Political Science and International Relations University of Southern California Los Angeles90007 United States School of Public Policy University of Southern California Los Angeles90007 United States University of Southern California Los Angeles90007 United States Department of Computer Science University of Southern California Los Angeles90007 United States Brain and Creativity Institute University of Southern California Los Angeles90007 United States Department of Sociology University of Southern California Los Angeles90007 United States Center for Global Health Science and Security Georgetown University WashingtonDC20057 United States Department of Electrical and Computer Engineering University of Southern California Los Angeles90007 United States The Lewis Registry Los AngelesCA90049 United States Department of Sociology The University of Texas at Austin Austin78712 United States Department of Psychology University of Southern California Los Angeles90007 United States Department of Political Science University of California Riverside Riverside92521 United States

Interactions between the government officials and civilians affect public wellbeing and the state legitimacy that is necessary for the functioning of democratic society. Police officers, the most visible and contacted agents of the state, interact with the public more than 20 million times a year during traffic stops. Today, these interactions are regularly recorded by body-worn cameras (BWCs), which are lauded as a means to enhance police accountability and improve police-public interactions. However, the timely analysis of these recordings is hampered by a lack of reliable automated tools that can enable the analysis of these complex and contested police-public interactions. This article proposes an approach to developing new multi-perspective, multimodal machine learning (ML) tools to analyze the audio, video, and transcript information from this BWC footage. Our approach begins by identifying the aspects of communication most salient to different stakeholders, including both community members and police officers. We move away from modeling approaches built around the existence of a single ground truth and instead utilize new advances in soft labeling to incorporate variation in how different observers perceive the same interactions. We argue that this inclusive approach to the conceptualization and design of new ML tools is broadly applicable to the study of communication and development of analytic tools across domains of human interaction, including education, medicine, and the workplace. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences

arXiv

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arXiv 2021年

作者： McGovern, Amy Ebert-Uphoff, Imme Gagne, David John Bostrom, Ann School of Computer Science and School of Meteorology University of Oklahoma NormanOK73019 United States Electrical and Computer Engineering Colorado State University Fort CollinsCO80523 United States Cooperative Institute for Research in the Atmosphere Colorado State University Fort CollinsCO80523 United States Computational and Information Systems Laboratory National Center for Atmospheric Research BoulderCO80301 United States Evans School of Public Policy & Governance University of Washington SeattleWA98195 United States

Given the growing use of Artificial Intelligence (AI) and machine learning (ML) methods across all aspects of environmental sciences, it is imperative that we initiate a discussion about the ethical and responsible use of AI. In fact, much can be learned from other domains where AI was introduced, often with the best of intentions, yet often led to unintended societal consequences, such as hard coding racial bias in the criminal justice system or increasing economic inequality through the financial system. A common misconception is that the environmental sciences are immune to such unintended consequences when AI is being used, as most data come from observations, and AI algorithms are based on mathematical formulas, which are often seen as objective. In this article, we argue the opposite can be the case. Using specific examples, we demonstrate many ways in which the use of AI can introduce similar consequences in the environmental sciences. This article will stimulate discussion and research efforts in this direction. As a community, we should avoid repeating any foreseeable mistakes made in other domains through the introduction of AI. In fact, with proper precautions, AI can be a great tool to help reduce climate and environmental injustice. We primarily focus on weather and climate examples but the conclusions apply broadly across the environmental sciences. © 2021, CC BY-SA.

关键词： Artificial intelligence

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Pandemic Vulnerability Index of US Cities: A Hybrid Knowledge-based and Data-driven Approach

arXiv

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arXiv 2022年

作者： Shahinoor Rahman, Md. Paul, Kamal Chandra Mokhlesur Rahman, Md. Samuel, Jim Thill, Jean-Claude Amjad Hossain, Md. Nawaz Ali, G.G.Md. Department of Earth and Environmental Sciences New Jersey City University Jersey CityNJ07305 United States Department of Electrical and Computer Engineering University of North Carolina at Charlotte CharlotteNC28223 United States The William States Lee College of Engineering University of North Carolina at Charlotte 9201 University City Blvd NC28223 United States E.J. Bloustein School of Planning & Public Policy Rutgers University NJ08901 United States Department of Geography and Earth Sciences University of North Carolina at Charlotte 9201 University City Blvd NC28223 United States Department of Accounting Information Systems and Finance Emporia State University EmporiaKS66801 United States Department of Computer Science and Information Systems Bradley University PeoriaIL61625 United States Khulna9203 Bangladesh School of Data Science University of North Carolina at Charlotte 9201 University City Blvd NC28223 United States

Cities become mission-critical zones during pandemics and it is vital to develop a better understanding of the factors that are associated with infection levels. The COVID-19 pandemic has impacted many cities severely;however, there is significant variance in its impact across cities. Pandemic infection levels are associated with inherent features of cities (e.g., population size, density, mobility patterns, socioeconomic condition, and health & environment), which need to be better understood. Intuitively, the infection levels are expected to be higher in big urban agglomerations, but the measurable influence of a specific urban feature is unclear. The present study examines 41 variables and their potential influence on COVID-19 cases and fatalities. The study uses a multi-method approach to study the influence of variables, classified as demographic, socioeconomic, mobility and connectivity, urban form and density, and health and environment dimensions. This study develops an index dubbed the PVI-CI for classifying the pandemic vulnerability levels of cities, grouping them into five vulnerability classes, from very high to very low. Furthermore, clustering and outlier analysis provides insights on the spatial clustering of cities with high and low vulnerability scores. This study provides strategic insights into levels of influence of key variables upon the spread of infections as well as fatalities, along with an objective ranking for the vulnerability of cities. Thus it provides critical wisdom needed for urban healthcare policy and resource management. The pandemic vulnerability index calculation method and the process present a blueprint for the development of similar indices for cities in other countries, leading to a better understanding and improved pandemic management for urban areas and post-pandemic urban planning across the world. © 2022, CC BY.

关键词： Population statistics

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The economics of utility-scale portable energy storage systems in a high-renewable grid

arXiv

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arXiv 2020年

作者： He, Guannan Michalek, Jeremy Kar, Soummya Chen, Qixin Zhang, Da Whitacre, Jay F. MIT Energy Initiative Massachusetts Institute of Technology CambridgeMA United States Institute of Energy Environment and Economy Tsinghua University Beijing China MIT Joint Program on the Science and Policy of Global Change Massachusetts Institute of Technology CambridgeMA United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States Department of Electrical Engineering Tsinghua University Beijing China Department of Engineering and Public Policy Carnegie Mellon University PittsburghPA United States Department of Mechanical Engineering Carnegie Mellon University PittsburghPA United States Department of Materials Science and Engineering Carnegie Mellon University PittsburghPA United States

Summary: Battery storage is expected to play a crucial role in the low-carbon transformation of energy systems. The deployment of battery storage in the power gird, however, is currently severely limited by its low economic viability, which results from not only high capital costs but also the lack of flexible and efficient utilization schemes and business models. Making utility-scale battery storage portable through trucking unlocks its capability to provide various on-demand services. We introduce the potential applications of utility-scale portable energy storage and investigate its economics in California using a spatiotemporal decision model that determines the optimal operation and transportation schedules of portable storage. We show that mobilizing energy storage can increase its life-cycle revenues by 70% in some areas and improve renewable energy integration by relieving local transmission congestion. The life-cycle revenue of spatiotemporal arbitrage can fully compensate for the costs of portable energy storage system in several regions in California, including San Diego and the San Francisco Bay Area. Copyright © 2020, The Authors. All rights reserved.

关键词： Life cycle

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Association Between Sex and Race and Ethnicity and IV Sedation Use in Patients Receiving Invasive Ventilation

CHEST Critical Care

引用

CHEST Critical Care 2024年第4期2卷 100100-100100页

作者： Walker, Sarah L. Angriman, Federico Burry, Lisa Celi, Leo Anthony Fiest, Kirsten M. Gichoya, Judy Johnson, Alistair Liu, Kuan Mehta, Sangeeta Roman-Sarita, Georgiana Seyyed-Kalantari, Laleh Vu, Thanh-Giang T. Whitlock, Elizabeth L. Tomlinson, George Yarnell, Christopher J. Institute of Health Policy Management and Evaluation University of Toronto Toronto ON Canada Interdepartmental Division of Critical Care Medicine University of Toronto Toronto ON Canada Leslie Dan Faculty of Pharmacy University of Toronto Toronto ON Canada University Health Network Toronto ON Canada Sinai Health Toronto ON Canada Department of Critical Care Medicine Scarborough Health Network Toronto ON Canada Scarborough Health Network Research Institute Toronto ON Canada Department of Electrical Engineering and Computer Science Lassonde School of Engineering York University Toronto ON Canada Vector Institute for Artificial Intelligence Toronto ON Canada Department of Critical Care Medicine University of Calgary Calgary AB Canada Department of Radiology and Biomedical Informatics Emory University Atlanta GA United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA Department of Anesthesia & Perioperative Care University of California San Francisco San Francisco CA Department of Anesthesiology & Pain Medicine University of Washington Seattle WA United States Department of Computer Science University of Toronto Toronto ON Canada Department of Respiratory Therapy University Health Network Toronto ON Canada

Background: IV sedation is an important tool for managing patients receiving invasive ventilation, yet excess sedation is harmful, and dosing could be influenced by implicit bias. Research Question: What are the associations between sex or race and ethnicity and sedation practices? Study Design and Methods: We performed a retrospective single-center cohort study of adults receiving invasive ventilation for ≥ 24 hours using the Medical Information Mart for Intensive Care Version IV (2008-2019) database from Boston, Massachusetts. We used a repeated-measures design (4-hour intervals) to study the association between sex (female or male) or race and ethnicity (Asian, Black, Hispanic, White) and sedation outcomes. Sedation outcomes included sedative use (propofol, benzodiazepine, dexmedetomidine) and minimum sedation score. We categorized sedative use as follows: no sedative and then lowest, second, third, and highest quartiles of sedative dose. We adjusted for covariates with multilevel Bayesian proportional odds modeling and reported ORs with 95% credible intervals (CrIs). Results: We studied 6,764 patients: 43% female;3.5% Asian, 12% Black, 4.5% Hispanic, and 80% White. Benzodiazepines were administered to 2,334 patients (36%). Black patients received benzodiazepines less often and at lower doses than White patients (more benzodiazepine: OR, 0.66;95% CrI, 0.49-0.92). Propofol was administered to 3,865 patients (57%). Female patients received propofol less often and at lower doses than male patients (more propofol: OR, 0.72;95% CrI, 0.61-0.86). Dexmedetomidine was administered to 1,439 patients (21%), and use was similar across sex or race and ethnicity. Female patients were less sedated than male patients (deeper sedation: OR, 0.71;95% CrI, 0.62-0.81), and Black patients were more sedated than White patients (more sedated: OR, 1.28;95% CrI, 1.05-1.55). Interpretation: Among patients receiving invasive ventilation for at least 24 hours, IV sedation and attained sedati

关键词： Bayesian modeling deep sedation differences in treatment by race or ethnicity differences in treatment by sex mechanical ventilation

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Simulating the charging of electric vehicles by laser

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Procedia computer Science 2019年 155卷 527-534页

作者： Yuvrajsinh Rathod Larry Hughes Yuvrajsinh Rathod MASc student Electrical and Computer Engineering Dalhousie University Halifax Canada Larry Hughes Founding Fellow MacEachen Insittute for Public Policy and Goverance Dalhousie University Halifax Canada

At present, wired and wireless charging methods for electric vehicles (EVs) have a number of drawbacks including short charging ranges and long charging times. Laser power transfer (LPT) is a wireless power transfer technique which can be used for UAV (unmanned aerial vehicle) and satellite charging. Recent developments in photovoltaic cells and laser technology allows the transfer power using light which might overcome many of the issues related with other charging methods. In this paper, we describe the design and implementation (in MATLAB) of a novel, high-power charging method using a laser (monochromatic light) to charge electric vehicles. The paper examines the overall efficiency of the LPT for a various input power-level. In addition, it also examines the safety and the possible charging infrastructure for LPT technology. Using this technique, we believe that an overall efficiency of 10-37% can be achieved using existing technology.

关键词： Electric vehicle LPT Wireless charging

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The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN)

arXiv

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arXiv 2024年

作者： Miao, Brenda Y. Chen, Irene Y. Williams, Christopher Y.K. Davidson, Jaysón Garcia-Agundez, Augusto Sun, Shenghuan Zack, Travis Saria, Suchi Arnaout, Rima Quer, Giorgio Sadaei, Hossein J. Torkamani, Ali Beaulieu-Jones, Brett Yu, Bin Gianfrancesco, Milena Butte, Atul J. Norgeot, Beau Sushil, Madhumita Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA United States UCSF-UC Berkeley Joint Program in Computational Precision Health University of California Berkeley University of California San Francisco BerkeleyCA United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States Berkeley AI Research University of California Berkeley BerkeleyCA United States Department of Medicine Division of Rheumatology University of California San Francisco San FranciscoCA United States Helen Diller Family Comprehensive Cancer Center University of California San Francisco San FranciscoCA United States Center for Data-driven Insights and Innovation University of California Office of the President OaklandCA United States Bayesian Health New YorkNY10282 United States Department of Computer Science Johns Hopkins University Whiting School of Engineering BaltimoreMD United States Department of Health Policy & Management Johns Hopkins University Bloomberg School of Public Health BaltimoreMD United States Department of Medicine Johns Hopkins Medicine BaltimoreMD21205 United States Departments of Medicine Radiology and Pediatrics University of California San Francisco San FranciscoCA United States Scripps Research Translational Institute La Jolla CA United States Department of Integrative Structural and Computational Biology Scripps Research La Jolla CA92037 United States Department of Medicine University of Chicago ChicagoIL United States Department of Statistics University of California Berkeley BerkeleyCA United States Center for Computational Biology University of California Berkeley BerkeleyCA United States Qualified Health PBC Palo AltoCA United States

Recent advances in generative models, including large language models (LLMs), vision language models (VLMs), and diffusion models, have accelerated the field of natural language and image processing in medicine and marked a significant paradigm shift in how biomedical models can be developed and deployed. While these models are highly adaptable to new tasks, scaling and evaluating their usage presents new challenges not addressed in previous frameworks. In particular, the ability of these models to produce useful outputs with little to no specialized training data ("zero-" or "few-shot" approaches), as well as the open-ended nature of their outputs, necessitate the development of new guidelines for robust reporting of clinical generative model research. In response to gaps in standards and best practices for the development of clinical AI tools identified by US Executive Order 141103 and several emerging national networks for clinical AI evaluation, we begin to formalize some of these guidelines by building on the original MI-CLAIM checklist. The new checklist, MI-CLAIM-GEN (Table 1), aims to address differences in training, evaluation, interpretability, and reproducibility of new generative models compared to non-generative ("predictive") AI models. This MI-CLAIM-GEN checklist also seeks to clarify cohort selection reporting with unstructured clinical data and adds additional items on alignment with ethical standards for clinical AI research. © 2024, CC BY.

关键词： Generative adversarial networks

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FTP1.7 Critical decision analysis of pre-hospital major haemorrhage protocol activation

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British Journal of Surgery 2023年第SUPPLEMENT_6期110卷

作者： Wohlgemut, Jared McMullen, Heather Marsden, Max Marsh, William Perkins, Zane Tai, Nigel Centre for Trauma Sciences Blizard Institute Queen Mary University of London London UK Global Public Health Unit Centre for Public Health and Policy Wolfson Institute of Population Health Queen Mary University of London London UK Department of Electrical Engineering and Computer Science Queen Mary University of London London UK London’s Air Ambulance 17th Floor Royal London Hospital London UK Trauma Service Royal London Hospital Barts Health NHS Trust London UK

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Capacity/cost trade-off for 5G small cell networks in the UHF and SHF bands

Capacity/cost trade-off for 5G small cell networks in the UH...

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International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Emanuel B. Teixeira Anderson R. Ramos Marisa S. Lourenço Fernando J. Velez Jon M. Peha Instituto de Telecomunicacções DEM Universidade da Beira Interior Covilhã Portugal Dept. of Electrical and Computer Engineering Dept. of Engineering & Public Policy Carnegie Mellon University Pittsburgh USA

ISBN: (数字)9781728154190

ISBN: (纸本)9781728154206

To fulfill the demand for high data rates from a wide number of users, higher frequencies can be used to provide the required capacity but with a different cost structure. 5G allows for very high data rates, which needs large bandwidths and requires very high throughput. This paper studies the economic trade-off of small cell networks deployed in Ultra High Frequency (UHF) and Super High Frequency (SHF) bands. Aiming at evaluating the variation of the carrier-to-noise-plus-interference ratio with the cell radius one considers the two-slope urban micro Line-of-Sight (UMiLoS) path loss model (ITU-R 2135 Report). The system capacity is analysed by the implicit function formulation to compute the supported cell throughput through the mapping of the PHY throughput into the step distances that correspond to the minimum carrier-to-interference-plus-noise ratio for each consecutive modulation and coding scheme. The variation of the equivalent supported throughput with the cell size at the 2.6, 3.5 and 5.62 GHz frequency bands facilitates to study the influence of the carrier-to-noise-plus-interference ratio in the dimensioning process. It is possible to conclude that, for the shortest cell radii, the profit is very low, starting to increase at a distance equal to the ratio between the break-point distance and the co-channel reuse factor attaining maxima for values of the cell radius approximately equal to 310, 390 and 740 m, at 2.6, 3.5 and 5.62 GHz, respectively (by assuming that the 5.62 GHz frequency band is unlicensed).

关键词： Throughput 5G mobile communication computer architecture Bandwidth Microprocessors Topology Modulation

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

引用

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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