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Lange M, Löwe A, Kayser I, Schaller A. Approaches for the Use of AI in Workplace Health Promotion and Prevention: Systematic Scoping Review. JMIR AI 2024; 3:e53506. [PMID: 38989904 PMCID: PMC11372327 DOI: 10.2196/53506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/02/2024] [Accepted: 07/10/2024] [Indexed: 07/12/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is an umbrella term for various algorithms and rapidly emerging technologies with huge potential for workplace health promotion and prevention (WHPP). WHPP interventions aim to improve people's health and well-being through behavioral and organizational measures or by minimizing the burden of workplace-related diseases and associated risk factors. While AI has been the focus of research in other health-related fields, such as public health or biomedicine, the transition of AI into WHPP research has yet to be systematically investigated. OBJECTIVE The systematic scoping review aims to comprehensively assess an overview of the current use of AI in WHPP. The results will be then used to point to future research directions. The following research questions were derived: (1) What are the study characteristics of studies on AI algorithms and technologies in the context of WHPP? (2) What specific WHPP fields (prevention, behavioral, and organizational approaches) were addressed by the AI algorithms and technologies? (3) What kind of interventions lead to which outcomes? METHODS A systematic scoping literature review (PRISMA-ScR [Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews]) was conducted in the 3 academic databases PubMed, Institute of Electrical and Electronics Engineers, and Association for Computing Machinery in July 2023, searching for papers published between January 2000 and December 2023. Studies needed to be (1) peer-reviewed, (2) written in English, and (3) focused on any AI-based algorithm or technology that (4) were conducted in the context of WHPP or (5) an associated field. Information on study design, AI algorithms and technologies, WHPP fields, and the patient or population, intervention, comparison, and outcomes framework were extracted blindly with Rayyan and summarized. RESULTS A total of 10 studies were included. Risk prevention and modeling were the most identified WHPP fields (n=6), followed by behavioral health promotion (n=4) and organizational health promotion (n=1). Further, 4 studies focused on mental health. Most AI algorithms were machine learning-based, and 3 studies used combined deep learning algorithms. AI algorithms and technologies were primarily implemented in smartphone apps (eg, in the form of a chatbot) or used the smartphone as a data source (eg, Global Positioning System). Behavioral approaches ranged from 8 to 12 weeks and were compared to control groups. Additionally, 3 studies evaluated the robustness and accuracy of an AI model or framework. CONCLUSIONS Although AI has caught increasing attention in health-related research, the review reveals that AI in WHPP is marginally investigated. Our results indicate that AI is promising for individualization and risk prediction in WHPP, but current research does not cover the scope of WHPP. Beyond that, future research will profit from an extended range of research in all fields of WHPP, longitudinal data, and reporting guidelines. TRIAL REGISTRATION OSF Registries osf.io/bfswp; https://osf.io/bfswp.
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Affiliation(s)
- Martin Lange
- Department of Fitness & Health, IST University of Applied Sciences, Duesseldorf, Germany
| | - Alexandra Löwe
- Department of Fitness & Health, IST University of Applied Sciences, Duesseldorf, Germany
| | - Ina Kayser
- Department of Communication & Business, IST University of Applied Sciences, Duesseldorf, Germany
| | - Andrea Schaller
- Institute of Sport Science, Department of Human Sciences, University of the Bundeswehr Munich, Munich, Germany
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Muralidharan V, Schamroth J, Youssef A, Celi LA, Daneshjou R. Applied artificial intelligence for global child health: Addressing biases and barriers. PLOS DIGITAL HEALTH 2024; 3:e0000583. [PMID: 39172772 PMCID: PMC11340888 DOI: 10.1371/journal.pdig.0000583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2024]
Abstract
Given the potential benefits of artificial intelligence and machine learning (AI/ML) within healthcare, it is critical to consider how these technologies can be deployed in pediatric research and practice. Currently, healthcare AI/ML has not yet adapted to the specific technical considerations related to pediatric data nor adequately addressed the specific vulnerabilities of children and young people (CYP) in relation to AI. While the greatest burden of disease in CYP is firmly concentrated in lower and middle-income countries (LMICs), existing applied pediatric AI/ML efforts are concentrated in a small number of high-income countries (HICs). In LMICs, use-cases remain primarily in the proof-of-concept stage. This narrative review identifies a number of intersecting challenges that pose barriers to effective AI/ML for CYP globally and explores the shifts needed to make progress across multiple domains. Child-specific technical considerations throughout the AI/ML lifecycle have been largely overlooked thus far, yet these can be critical to model effectiveness. Governance concerns are paramount, with suitable national and international frameworks and guidance required to enable the safe and responsible deployment of advanced technologies impacting the care of CYP and using their data. An ambitious vision for child health demands that the potential benefits of AI/Ml are realized universally through greater international collaboration, capacity building, strong oversight, and ultimately diffusing the AI/ML locus of power to empower researchers and clinicians globally. In order that AI/ML systems that do not exacerbate inequalities in pediatric care, teams researching and developing these technologies in LMICs must ensure that AI/ML research is inclusive of the needs and concerns of CYP and their caregivers. A broad, interdisciplinary, and human-centered approach to AI/ML is essential for developing tools for healthcare workers delivering care, such that the creation and deployment of ML is grounded in local systems, cultures, and clinical practice. Decisions to invest in developing and testing pediatric AI/ML in resource-constrained settings must always be part of a broader evaluation of the overall needs of a healthcare system, considering the critical building blocks underpinning effective, sustainable, and cost-efficient healthcare delivery for CYP.
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Affiliation(s)
- Vijaytha Muralidharan
- Department of Dermatology, Stanford University, Stanford, California, United States of America
| | - Joel Schamroth
- Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - Alaa Youssef
- Stanford Center for Artificial Intelligence in Medicine and Imaging, Department of Radiology, Stanford University, Stanford, California, United States of America
| | - Leo A. Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Roxana Daneshjou
- Department of Dermatology, Stanford University, Stanford, California, United States of America
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
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3
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Wen D, Soltan A, Trucco E, Matin RN. From data to diagnosis: skin cancer image datasets for artificial intelligence. Clin Exp Dermatol 2024; 49:675-685. [PMID: 38549552 DOI: 10.1093/ced/llae112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/11/2024] [Accepted: 03/25/2024] [Indexed: 06/26/2024]
Abstract
Artificial intelligence (AI) solutions for skin cancer diagnosis continue to gain momentum, edging closer towards broad clinical use. These AI models, particularly deep-learning architectures, require large digital image datasets for development. This review provides an overview of the datasets used to develop AI algorithms and highlights the importance of dataset transparency for the evaluation of algorithm generalizability across varying populations and settings. Current challenges for curation of clinically valuable datasets are detailed, which include dataset shifts arising from demographic variations and differences in data collection methodologies, along with inconsistencies in labelling. These shifts can lead to differential algorithm performance, compromise of clinical utility, and the propagation of discriminatory biases when developed algorithms are implemented in mismatched populations. Limited representation of rare skin cancers and minoritized groups in existing datasets are highlighted, which can further skew algorithm performance. Strategies to address these challenges are presented, which include improving transparency, representation and interoperability. Federated learning and generative methods, which may improve dataset size and diversity without compromising privacy, are also examined. Lastly, we discuss model-level techniques that may address biases entrained through the use of datasets derived from routine clinical care. As the role of AI in skin cancer diagnosis becomes more prominent, ensuring the robustness of underlying datasets is increasingly important.
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Affiliation(s)
- David Wen
- Department of Dermatology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Oxford University Clinical Academic Graduate School, University of Oxford, Oxford, UK
| | - Andrew Soltan
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
- Oxford Cancer and Haematology Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Department of Oncology, University of Oxford, Oxford, UK
| | - Emanuele Trucco
- VAMPIRE Project, Computing, School of Science and Engineering, University of Dundee, Dundee, UK
| | - Rubeta N Matin
- Department of Dermatology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Artificial Intelligence Working Party Group, British Association of Dermatologists, London, UK
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4
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Wang W, Huang H, Peng X, Wang Z, Zeng Y. Utilizing support vector machines to foster sustainable development and innovation in the clean energy sector via green finance. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 360:121225. [PMID: 38796867 DOI: 10.1016/j.jenvman.2024.121225] [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: 12/28/2023] [Revised: 04/26/2024] [Accepted: 05/21/2024] [Indexed: 05/29/2024]
Abstract
As the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. in order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. through the use of Support Vector Machine (SVM) Multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. with wind power base a as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terAs the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. In order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. Through the use of Support Vector Machine (SVM) multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. With Wind Power Base A as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terms of the number of units, internal rate of return within the project, and annual average equivalent utilization hours at Wind Power Base A. The number of units decreases to 5860, with total purchase and transportation costs remaining basically unchanged, the internal rate of return increases to 9.3%, and annual equivalent utilization hours increase to 2044 h. Energy consumption and CO2 emissions are significantly reduced, with energy consumption decreasing by 0.68 × 109 kgce and CO2 emissions decreasing by 1.29 × 109 kg. The optimization effects are mainly concentrated in the production and installation stages, with emission reductions achieved through the recycling and disposal of materials consumed in the early stages. In terms of investment benefits, environmental benefits are enhanced, with a 13.93% reduction in CO2 emissions. Moreover, there is improved energy efficiency, with the energy input-output ratio increasing from 7.73 to 9.31. This indicates that the Wind Power Base A project has significant environmental and energy efficiency advantages in the clean energy industry. This work innovatively provides a comprehensive assessment and optimization scheme for clean energy projects and predicts the profitability of Wind Power Base A using SVM multi-factor models. Besides, this work optimizes key parameters of the project using a bi-level multi-objective approach, thus comprehensively improving the investment return and utilization efficiency of the wind power base. This work provides innovative methods and strong data support for the development of the clean energy industry, which is of great significance for promoting sustainable development under the backdrop of green finance.
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Affiliation(s)
- Weijia Wang
- School of Information Technology, Deakin University, Geelong, VIC, 3216, Australia.
| | - Huimin Huang
- School of Public Administration, Guangzhou University, Guangzhou, 510006, China.
| | - Xiaoyan Peng
- School of Government, Sun Yat-sen University, Guangzhou, 510275, China.
| | - Zeyu Wang
- School of Public Administration, Guangzhou University, Guangzhou, 510006, China.
| | - Yanzhao Zeng
- School of Economics and Statistics, Guangzhou University, Guangzhou, 510006, China.
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5
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Yang H, Zhu D, He S, Xu Z, Liu Z, Zhang W, Cai J. Enhancing psychiatric rehabilitation outcomes through a multimodal multitask learning model based on BERT and TabNet: An approach for personalized treatment and improved decision-making. Psychiatry Res 2024; 336:115896. [PMID: 38626625 DOI: 10.1016/j.psychres.2024.115896] [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: 06/26/2023] [Revised: 04/03/2024] [Accepted: 04/05/2024] [Indexed: 04/18/2024]
Abstract
Evaluating the rehabilitation status of individuals with serious mental illnesses (SMI) necessitates a comprehensive analysis of multimodal data, including unstructured text records and structured diagnostic data. However, progress in the effective assessment of rehabilitation status remains limited. Our study develops a deep learning model integrating Bidirectional Encoder Representations from Transformers (BERT) and TabNet through a late fusion strategy to enhance rehabilitation prediction, including referral risk, dangerous behaviors, self-awareness, and medication adherence, in patients with SMI. BERT processes unstructured textual data, such as doctor's notes, whereas TabNet manages structured diagnostic information. The model's interpretability function serves to assist healthcare professionals in understanding the model's predictive decisions, improving patient care. Our model exhibited excellent predictive performance for all four tasks, with an accuracy exceeding 0.78 and an area under the curve of 0.70. In addition, a series of tests proved the model's robustness, fairness, and interpretability. This study combines multimodal and multitask learning strategies into a model and applies it to rehabilitation assessment tasks, offering a promising new tool that can be seamlessly integrated with the clinical workflow to support the provision of optimized patient care.
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Affiliation(s)
- Hongyi Yang
- School of Design, Shanghai Jiao Tong University, Shanghai, China
| | - Dian Zhu
- School of Design, Shanghai Jiao Tong University, Shanghai, China
| | - Siyuan He
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhiqi Xu
- School of Design, Shanghai Jiao Tong University, Shanghai, China
| | - Zhao Liu
- School of Design, Shanghai Jiao Tong University, Shanghai, China.
| | - Weibo Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China; Mental Health Branch, China Hospital Development Institute, Shanghai Jiao Tong University, Shanghai, China.
| | - Jun Cai
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Mental Health Branch, China Hospital Development Institute, Shanghai Jiao Tong University, Shanghai, China.
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6
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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] [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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7
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Huguet N, Chen J, Parikh RB, Marino M, Flocke SA, Likumahuwa-Ackman S, Bekelman J, DeVoe JE. Applying Machine Learning Techniques to Implementation Science. Online J Public Health Inform 2024; 16:e50201. [PMID: 38648094 DOI: 10.2196/50201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 11/15/2023] [Accepted: 03/14/2024] [Indexed: 04/25/2024] Open
Abstract
Machine learning (ML) approaches could expand the usefulness and application of implementation science methods in clinical medicine and public health settings. The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, such as predicting what will work best, for whom, under what circumstances, and with what predicted level of support, and what and when adaptation or deimplementation are needed. We describe how ML approaches could be used and discuss challenges that implementation scientists and methodologists will need to consider when using ML throughout the stages of implementation.
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Affiliation(s)
- Nathalie Huguet
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Jinying Chen
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Data Science Core, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- iDAPT Implementation Science Center for Cancer Control, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Ravi B Parikh
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Miguel Marino
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Susan A Flocke
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Sonja Likumahuwa-Ackman
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Justin Bekelman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, United States
| | - Jennifer E DeVoe
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
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Tamrat T, Zhao Y, Schalet D, AlSalamah S, Pujari S, Say L. Exploring the Use and Implications of AI in Sexual and Reproductive Health and Rights: Protocol for a Scoping Review. JMIR Res Protoc 2024; 13:e53888. [PMID: 38593433 PMCID: PMC11040437 DOI: 10.2196/53888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/23/2024] [Accepted: 02/09/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has emerged as a transformative force across the health sector and has garnered significant attention within sexual and reproductive health and rights (SRHR) due to polarizing views on its opportunities to advance care and the heightened risks and implications it brings to people's well-being and bodily autonomy. As the fields of AI and SRHR evolve, clarity is needed to bridge our understanding of how AI is being used within this historically politicized health area and raise visibility on the critical issues that can facilitate its responsible and meaningful use. OBJECTIVE This paper presents the protocol for a scoping review to synthesize empirical studies that focus on the intersection of AI and SRHR. The review aims to identify the characteristics of AI systems and tools applied within SRHR, regarding health domains, intended purpose, target users, AI data life cycle, and evidence on benefits and harms. METHODS The scoping review follows the standard methodology developed by Arksey and O'Malley. We will search the following electronic databases: MEDLINE (PubMed), Scopus, Web of Science, and CINAHL. Inclusion criteria comprise the use of AI systems and tools in sexual and reproductive health and clear methodology describing either quantitative or qualitative approaches, including program descriptions. Studies will be excluded if they focus entirely on digital interventions that do not explicitly use AI systems and tools, are about robotics or nonhuman subjects, or are commentaries. We will not exclude articles based on geographic location, language, or publication date. The study will present the uses of AI across sexual and reproductive health domains, the intended purpose of the AI system and tools, and maturity within the AI life cycle. Outcome measures will be reported on the effect, accuracy, acceptability, resource use, and feasibility of studies that have deployed and evaluated AI systems and tools. Ethical and legal considerations, as well as findings from qualitative studies, will be synthesized through a narrative thematic analysis. We will use the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) format for the publication of the findings. RESULTS The database searches resulted in 12,793 records when the searches were conducted in October 2023. Screening is underway, and the analysis is expected to be completed by July 2024. CONCLUSIONS The findings will provide key insights on usage patterns and evidence on the use of AI in SRHR, as well as convey key ethical, safety, and legal considerations. The outcomes of this scoping review are contributing to a technical brief developed by the World Health Organization and will guide future research and practice in this highly charged area of work. TRIAL REGISTRATION OSF Registries osf.io/ma4d9; https://osf.io/ma4d9. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/53888.
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Affiliation(s)
- Tigest Tamrat
- UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction, Department of Sexual and Reproductive Health and Research, World Health Organization, Geneva, Switzerland
| | - Yu Zhao
- Department of Digital Health and Innovations, Science Division, World Health Organization, Geneva, Switzerland
| | - Denise Schalet
- Department of Digital Health and Innovations, Science Division, World Health Organization, Geneva, Switzerland
| | - Shada AlSalamah
- Department of Digital Health and Innovations, Science Division, World Health Organization, Geneva, Switzerland
- Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Sameer Pujari
- Department of Digital Health and Innovations, Science Division, World Health Organization, Geneva, Switzerland
| | - Lale Say
- UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction, Department of Sexual and Reproductive Health and Research, World Health Organization, Geneva, Switzerland
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Pinsky MR, Bedoya A, Bihorac A, Celi L, Churpek M, Economou-Zavlanos NJ, Elbers P, Saria S, Liu V, Lyons PG, Shickel B, Toral P, Tscholl D, Clermont G. Use of artificial intelligence in critical care: opportunities and obstacles. Crit Care 2024; 28:113. [PMID: 38589940 PMCID: PMC11000355 DOI: 10.1186/s13054-024-04860-z] [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: 02/22/2024] [Accepted: 03/05/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven. Major obstacles to widespread effective application of AI approaches to the real-time care of the critically ill patient exist and need to be addressed. MAIN BODY Clinical decision support systems (CDSSs) in acute and critical care environments support clinicians, not replace them at the bedside. As will be discussed in this review, the reasons are many and include the immaturity of AI-based systems to have situational awareness, the fundamental bias in many large databases that do not reflect the target population of patient being treated making fairness an important issue to address and technical barriers to the timely access to valid data and its display in a fashion useful for clinical workflow. The inherent "black-box" nature of many predictive algorithms and CDSS makes trustworthiness and acceptance by the medical community difficult. Logistically, collating and curating in real-time multidimensional data streams of various sources needed to inform the algorithms and ultimately display relevant clinical decisions support format that adapt to individual patient responses and signatures represent the efferent limb of these systems and is often ignored during initial validation efforts. Similarly, legal and commercial barriers to the access to many existing clinical databases limit studies to address fairness and generalizability of predictive models and management tools. CONCLUSIONS AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development.
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Affiliation(s)
- Michael R Pinsky
- Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, 638 Scaife Hall, 3550 Terrace Street, Pittsburgh, PA, 15261, USA.
| | - Armando Bedoya
- Algorithm-Based Clinical Decision Support (ABCDS) Oversight, Office of Vice Dean of Data Science, School of Medicine, Duke University, Durham, NC, 27705, USA
- Division of Pulmonary Critical Care Medicine, Duke University School of Medicine, Durham, NC, 27713, USA
| | - Azra Bihorac
- Department of Medicine, University of Florida College of Medicine Gainesville, Malachowsky Hall, 1889 Museum Road, Suite 2410, Gainesville, FL, 32611, USA
| | - Leo Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Matthew Churpek
- Department of Medicine, University of Wisconsin, 600 Highland Ave, Madison, WI, 53792, USA
| | - Nicoleta J Economou-Zavlanos
- Algorithm-Based Clinical Decision Support (ABCDS) Oversight, Office of Vice Dean of Data Science, School of Medicine, Duke University, Durham, NC, 27705, USA
| | - Paul Elbers
- Department of Intensive Care, Amsterdam UMC, Amsterdam, NL, USA
- Amsterdam UMC, ZH.7D.167, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Suchi Saria
- Department of Computer Science, Whiting School of Engineering, Johns Hopkins Medical Institutions, Johns Hopkins University, 333 Malone Hall, 300 Wolfe Street, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins School of Medicine, AI and Health Lab, Johns Hopkins University, Baltimore, MD, USA
- Bayesian Health, New york, NY, 10282, USA
| | - Vincent Liu
- Department of Medicine, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Road, Mail Code UHN67, Portland, OR, 97239-3098, USA
- , 2000 Broadway, Oakland, CA, 94612, USA
| | - Patrick G Lyons
- Department of Medicine, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Road, Mail Code UHN67, Portland, OR, 97239-3098, USA
| | - Benjamin Shickel
- Department of Medicine, University of Florida College of Medicine Gainesville, Malachowsky Hall, 1889 Museum Road, Suite 2410, Gainesville, FL, 32611, USA
- Amsterdam UMC, ZH.7D.167, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Patrick Toral
- Department of Intensive Care, Amsterdam UMC, Amsterdam, NL, USA
- Amsterdam UMC, ZH.7D.165, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - David Tscholl
- Institute of Anesthesiology, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Gilles Clermont
- Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, 638 Scaife Hall, 3550 Terrace Street, Pittsburgh, PA, 15261, USA
- VA Pittsburgh Health System, 131A Building 30, 4100 Allequippa St, Pittsburgh, PA, 15240, USA
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10
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Wimbarti S, Kairupan BHR, Tallei TE. Critical review of self-diagnosis of mental health conditions using artificial intelligence. Int J Ment Health Nurs 2024; 33:344-358. [PMID: 38345132 DOI: 10.1111/inm.13303] [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: 09/20/2023] [Revised: 01/26/2024] [Accepted: 01/30/2024] [Indexed: 03/10/2024]
Abstract
The advent of artificial intelligence (AI) has revolutionised various aspects of our lives, including mental health nursing. AI-driven tools and applications have provided a convenient and accessible means for individuals to assess their mental well-being within the confines of their homes. Nonetheless, the widespread trend of self-diagnosing mental health conditions through AI poses considerable risks. This review article examines the perils associated with relying on AI for self-diagnosis in mental health, highlighting the constraints and possible adverse outcomes that can arise from such practices. It delves into the ethical, psychological, and social implications, underscoring the vital role of mental health professionals, including psychologists, psychiatrists, and nursing specialists, in providing professional assistance and guidance. This article aims to highlight the importance of seeking professional assistance and guidance in addressing mental health concerns, especially in the era of AI-driven self-diagnosis.
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Affiliation(s)
- Supra Wimbarti
- Faculty of Psychology, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - B H Ralph Kairupan
- Department of Psychiatry, Faculty of Medicine, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
| | - Trina Ekawati Tallei
- Department of Biology, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
- Department of Biology, Faculty of Medicine, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
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11
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Trinkley KE, An R, Maw AM, Glasgow RE, Brownson RC. Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions. Implement Sci 2024; 19:17. [PMID: 38383393 PMCID: PMC10880216 DOI: 10.1186/s13012-024-01346-y] [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: 10/30/2023] [Accepted: 01/25/2024] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms. The increasing availability of artificial intelligence applications offers opportunities to help address specific issues faced by the field of implementation science and expand its methods. MAIN TEXT This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence. We answer the questions of "why" the field of implementation science should consider artificial intelligence, for "what" (the purpose and methods), and the "what" (consequences and challenges). We describe specific ways artificial intelligence can address implementation science challenges related to (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Examples are provided from global health systems, public health, and precision health that illustrate both potential advantages and hazards of integrating artificial intelligence applications into implementation science methods. We conclude by providing recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly. CONCLUSIONS Artificial intelligence holds promise to advance implementation science methods ("why") and accelerate its goals of closing the evidence-to-practice gap ("purpose"). However, evaluation of artificial intelligence's potential unintended consequences must be considered and proactively monitored. Given the technical nature of artificial intelligence applications as well as their potential impact on the field, transdisciplinary collaboration is needed and may suggest the need for a subset of implementation scientists cross-trained in both fields to ensure artificial intelligence is used optimally and ethically.
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Affiliation(s)
- Katy E Trinkley
- Department of Family Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Adult and Child Center for Outcomes Research and Delivery Science Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Colorado Center for Personalized Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Ruopeng An
- Brown School and Division of Computational and Data Sciences at Washington University in St. Louis, St. Louis, MO, USA
| | - Anna M Maw
- Adult and Child Center for Outcomes Research and Delivery Science Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- School of Medicine, Division of Hospital Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Russell E Glasgow
- Department of Family Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Adult and Child Center for Outcomes Research and Delivery Science Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Ross C Brownson
- Prevention Research Center, Brown School at Washington University in St. Louis, St. Louis, MO, USA
- Department of Surgery, Division of Public Health Sciences, and Alvin J. Siteman Cancer Center, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
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12
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Cai Y, Cai YQ, Tang LY, Wang YH, Gong M, Jing TC, Li HJ, Li-Ling J, Hu W, Yin Z, Gong DX, Zhang GW. Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review. BMC Med 2024; 22:56. [PMID: 38317226 PMCID: PMC10845808 DOI: 10.1186/s12916-024-03273-7] [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: 07/16/2023] [Accepted: 01/23/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation. METHODS PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789). RESULTS In 20,887 screened references, 79 articles (82.5% in 2017-2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5-52,000, median 21) and large-span sample size (range 80-3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as "recommended"; however, 281 and 187 were "not recommended" and "warning," respectively. CONCLUSION AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.
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Affiliation(s)
- Yue Cai
- China Medical University, Shenyang, 110122, China
| | - Yu-Qing Cai
- China Medical University, Shenyang, 110122, China
| | - Li-Ying Tang
- China Medical University, Shenyang, 110122, China
| | - Yi-Han Wang
- China Medical University, Shenyang, 110122, China
| | - Mengchun Gong
- Digital Health China Co. Ltd, Beijing, 100089, China
| | - Tian-Ci Jing
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Hui-Jun Li
- Shenyang Medical & Film Science and Technology Co. Ltd., Shenyang, 110001, China
- Enduring Medicine Smart Innovation Research Institute, Shenyang, 110001, China
| | - Jesse Li-Ling
- Institute of Genetic Medicine, School of Life Science, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, 610065, China
| | - Wei Hu
- Bayi Orthopedic Hospital, Chengdu, 610017, China
| | - Zhihua Yin
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, 110122, China.
| | - Da-Xin Gong
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China.
- The Internet Hospital Branch of the Chinese Research Hospital Association, Beijing, 100006, China.
| | - Guang-Wei Zhang
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China.
- The Internet Hospital Branch of the Chinese Research Hospital Association, Beijing, 100006, China.
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13
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Muralidharan V, Tran MM, Barrios L, Beams B, Ko JM, Siegel DH, Bailenson J. Best Practices for Research in Virtual and Augmented Reality in Dermatology. J Invest Dermatol 2024; 144:17-23. [PMID: 38105083 DOI: 10.1016/j.jid.2023.10.014] [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: 06/27/2023] [Revised: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 12/19/2023]
Abstract
Virtual reality (VR) and augmented reality (AR) technologies have advanced rapidly in recent years. These cutting-edge technologies provide dermatology researchers, educators, proceduralists, and patients with opportunities in new scientific horizons. VR is a technology that facilitates immersive human experiences by allowing users to connect with various simulated environments through natural head and hand movements, whereas AR supplements a user's perception of their real environment with virtual elements. Despite technological advancements, there is limited literature on the methodological steps for conducting rigorous VR and AR research in dermatology. Effective storyboarding, user-driven design, and interdisciplinary teamwork play a central role in ensuring that VR/AR applications meet the specific needs of dermatology clinical and research teams. We present a step-by-step approach for their design, team composition, and evaluation in dermatology research, medical education, procedures, and habit formation strategies. We also discuss current VR and AR dermatology applications and the importance of ethical and safety considerations in deploying this new technology.
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Affiliation(s)
- Vijaytha Muralidharan
- Department of Dermatology, Stanford University School of Medicine, Redwood City, California, USA.
| | - Megan M Tran
- The Warren Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Laurel Barrios
- School of Medicine, University of California Davis, Davis, California
| | - Brian Beams
- Stanford Virtual Human Interaction Lab, Stanford, California, USA
| | - Justin M Ko
- Department of Dermatology, Stanford University School of Medicine, Redwood City, California, USA
| | - Dawn H Siegel
- Department of Dermatology, Stanford University School of Medicine, Redwood City, California, USA
| | - Jeremy Bailenson
- Stanford Virtual Human Interaction Lab, Stanford, California, USA
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Chin MH, Afsar-Manesh N, Bierman AS, Chang C, Colón-Rodríguez CJ, Dullabh P, Duran DG, Fair M, Hernandez-Boussard T, Hightower M, Jain A, Jordan WB, Konya S, Moore RH, Moore TT, Rodriguez R, Shaheen G, Snyder LP, Srinivasan M, Umscheid CA, Ohno-Machado L. Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care. JAMA Netw Open 2023; 6:e2345050. [PMID: 38100101 PMCID: PMC11181958 DOI: 10.1001/jamanetworkopen.2023.45050] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2023] Open
Abstract
Importance Health care algorithms are used for diagnosis, treatment, prognosis, risk stratification, and allocation of resources. Bias in the development and use of algorithms can lead to worse outcomes for racial and ethnic minoritized groups and other historically marginalized populations such as individuals with lower income. Objective To provide a conceptual framework and guiding principles for mitigating and preventing bias in health care algorithms to promote health and health care equity. Evidence Review The Agency for Healthcare Research and Quality and the National Institute for Minority Health and Health Disparities convened a diverse panel of experts to review evidence, hear from stakeholders, and receive community feedback. Findings The panel developed a conceptual framework to apply guiding principles across an algorithm's life cycle, centering health and health care equity for patients and communities as the goal, within the wider context of structural racism and discrimination. Multiple stakeholders can mitigate and prevent bias at each phase of the algorithm life cycle, including problem formulation (phase 1); data selection, assessment, and management (phase 2); algorithm development, training, and validation (phase 3); deployment and integration of algorithms in intended settings (phase 4); and algorithm monitoring, maintenance, updating, or deimplementation (phase 5). Five principles should guide these efforts: (1) promote health and health care equity during all phases of the health care algorithm life cycle; (2) ensure health care algorithms and their use are transparent and explainable; (3) authentically engage patients and communities during all phases of the health care algorithm life cycle and earn trustworthiness; (4) explicitly identify health care algorithmic fairness issues and trade-offs; and (5) establish accountability for equity and fairness in outcomes from health care algorithms. Conclusions and Relevance Multiple stakeholders must partner to create systems, processes, regulations, incentives, standards, and policies to mitigate and prevent algorithmic bias. Reforms should implement guiding principles that support promotion of health and health care equity in all phases of the algorithm life cycle as well as transparency and explainability, authentic community engagement and ethical partnerships, explicit identification of fairness issues and trade-offs, and accountability for equity and fairness.
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Affiliation(s)
| | | | | | - Christine Chang
- Agency for Healthcare Research and Quality, Rockville, Maryland
| | | | | | | | - Malika Fair
- Association of American Medical Colleges, Washington, DC
| | | | | | - Anjali Jain
- Agency for Healthcare Research and Quality, Rockville, Maryland
| | | | - Stephen Konya
- Office of the National Coordinator for Health Information Technology, Washington, DC
| | - Roslyn Holliday Moore
- US Department of Health and Human Services Office of Minority Health, Rockville, Maryland
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15
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Ng MY, Youssef A, Miner AS, Sarellano D, Long J, Larson DB, Hernandez-Boussard T, Langlotz CP. Perceptions of Data Set Experts on Important Characteristics of Health Data Sets Ready for Machine Learning: A Qualitative Study. JAMA Netw Open 2023; 6:e2345892. [PMID: 38039004 PMCID: PMC10692863 DOI: 10.1001/jamanetworkopen.2023.45892] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/20/2023] [Indexed: 12/02/2023] Open
Abstract
Importance The lack of data quality frameworks to guide the development of artificial intelligence (AI)-ready data sets limits their usefulness for machine learning (ML) research in health care and hinders the diagnostic excellence of developed clinical AI applications for patient care. Objective To discern what constitutes high-quality and useful data sets for health and biomedical ML research purposes according to subject matter experts. Design, Setting, and Participants This qualitative study interviewed data set experts, particularly those who are creators and ML researchers. Semistructured interviews were conducted in English and remotely through a secure video conferencing platform between August 23, 2022, and January 5, 2023. A total of 93 experts were invited to participate. Twenty experts were enrolled and interviewed. Using purposive sampling, experts were affiliated with a diverse representation of 16 health data sets/databases across organizational sectors. Content analysis was used to evaluate survey information and thematic analysis was used to analyze interview data. Main Outcomes and Measures Data set experts' perceptions on what makes data sets AI ready. Results Participants included 20 data set experts (11 [55%] men; mean [SD] age, 42 [11] years), of whom all were health data set creators, and 18 of the 20 were also ML researchers. Themes (3 main and 11 subthemes) were identified and integrated into an AI-readiness framework to show their association within the health data ecosystem. Participants partially determined the AI readiness of data sets using priority appraisal elements of accuracy, completeness, consistency, and fitness. Ethical acquisition and societal impact emerged as appraisal considerations in that participant samples have not been described to date in prior data quality frameworks. Factors that drive creation of high-quality health data sets and mitigate risks associated with data reuse in ML research were also relevant to AI readiness. The state of data availability, data quality standards, documentation, team science, and incentivization were associated with elements of AI readiness and the overall perception of data set usefulness. Conclusions and Relevance In this qualitative study of data set experts, participants contributed to the development of a grounded framework for AI data set quality. Data set AI readiness required the concerted appraisal of many elements and the balancing of transparency and ethical reflection against pragmatic constraints. The movement toward more reliable, relevant, and ethical AI and ML applications for patient care will inevitably require strategic updates to data set creation practices.
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Affiliation(s)
- Madelena Y. Ng
- Department of Medicine (Biomedical Informatics), Stanford University School of Medicine, Stanford, California
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California
| | - Alaa Youssef
- Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Adam S. Miner
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
| | - Daniela Sarellano
- Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Jin Long
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - David B. Larson
- Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Tina Hernandez-Boussard
- Department of Medicine (Biomedical Informatics), Stanford University School of Medicine, Stanford, California
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California
| | - Curtis P. Langlotz
- Department of Medicine (Biomedical Informatics), Stanford University School of Medicine, Stanford, California
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California
- Department of Radiology, Stanford University School of Medicine, Stanford, California
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Overgaard SM, Graham MG, Brereton T, Pencina MJ, Halamka JD, Vidal DE, Economou-Zavlanos NJ. Implementing quality management systems to close the AI translation gap and facilitate safe, ethical, and effective health AI solutions. NPJ Digit Med 2023; 6:218. [PMID: 38007604 PMCID: PMC10676432 DOI: 10.1038/s41746-023-00968-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 11/15/2023] [Indexed: 11/27/2023] Open
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17
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Moazemi S, Vahdati S, Li J, Kalkhoff S, Castano LJV, Dewitz B, Bibo R, Sabouniaghdam P, Tootooni MS, Bundschuh RA, Lichtenberg A, Aubin H, Schmid F. Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review. Front Med (Lausanne) 2023; 10:1109411. [PMID: 37064042 PMCID: PMC10102653 DOI: 10.3389/fmed.2023.1109411] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 03/10/2023] [Indexed: 04/03/2023] Open
Abstract
Background Artificial intelligence (AI) and machine learning (ML) models continue to evolve the clinical decision support systems (CDSS). However, challenges arise when it comes to the integration of AI/ML into clinical scenarios. In this systematic review, we followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), the population, intervention, comparator, outcome, and study design (PICOS), and the medical AI life cycle guidelines to investigate studies and tools which address AI/ML-based approaches towards clinical decision support (CDS) for monitoring cardiovascular patients in intensive care units (ICUs). We further discuss recent advances, pitfalls, and future perspectives towards effective integration of AI into routine practices as were identified and elaborated over an extensive selection process for state-of-the-art manuscripts. Methods Studies with available English full text from PubMed and Google Scholar in the period from January 2018 to August 2022 were considered. The manuscripts were fetched through a combination of the search keywords including AI, ML, reinforcement learning (RL), deep learning, clinical decision support, and cardiovascular critical care and patients monitoring. The manuscripts were analyzed and filtered based on qualitative and quantitative criteria such as target population, proper study design, cross-validation, and risk of bias. Results More than 100 queries over two medical search engines and subjective literature research were developed which identified 89 studies. After extensive assessments of the studies both technically and medically, 21 studies were selected for the final qualitative assessment. Discussion Clinical time series and electronic health records (EHR) data were the most common input modalities, while methods such as gradient boosting, recurrent neural networks (RNNs) and RL were mostly used for the analysis. Seventy-five percent of the selected papers lacked validation against external datasets highlighting the generalizability issue. Also, interpretability of the AI decisions was identified as a central issue towards effective integration of AI in healthcare.
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Affiliation(s)
- Sobhan Moazemi
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | - Sahar Vahdati
- Institute for Applied Informatics (InfAI), Dresden, Germany
| | - Jason Li
- Institute for Applied Informatics (InfAI), Dresden, Germany
| | - Sebastian Kalkhoff
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | - Luis J. V. Castano
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | - Bastian Dewitz
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | - Roman Bibo
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | | | - Mohammad S. Tootooni
- Department of Health Informatics and Data Science, Loyola University Chicago, Chicago, IL, United States
| | - Ralph A. Bundschuh
- Nuclear Medicine, Medical Faculty, University Augsburg, Augsburg, Germany
| | - Artur Lichtenberg
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | - Hug Aubin
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | - Falko Schmid
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
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Chen Y, Clayton EW, Novak LL, Anders S, Malin B. Human-Centered Design to Address Biases in Artificial Intelligence. J Med Internet Res 2023; 25:e43251. [PMID: 36961506 PMCID: PMC10132017 DOI: 10.2196/43251] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 01/30/2023] [Accepted: 02/28/2023] [Indexed: 03/25/2023] Open
Abstract
The potential of artificial intelligence (AI) to reduce health care disparities and inequities is recognized, but it can also exacerbate these issues if not implemented in an equitable manner. This perspective identifies potential biases in each stage of the AI life cycle, including data collection, annotation, machine learning model development, evaluation, deployment, operationalization, monitoring, and feedback integration. To mitigate these biases, we suggest involving a diverse group of stakeholders, using human-centered AI principles. Human-centered AI can help ensure that AI systems are designed and used in a way that benefits patients and society, which can reduce health disparities and inequities. By recognizing and addressing biases at each stage of the AI life cycle, AI can achieve its potential in health care.
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Affiliation(s)
- You Chen
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Ellen Wright Clayton
- Law School, Vanderbilt University, Nashville, TN, United States
- Center for Biomedical Ethics and Society, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Laurie Lovett Novak
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Shilo Anders
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN, United States
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Bradley Malin
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN, United States
- Center for Biomedical Ethics and Society, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
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Taber P, Armin JS, Orozco G, Del Fiol G, Erdrich J, Kawamoto K, Israni ST. Artificial Intelligence and Cancer Control: Toward Prioritizing Justice, Equity, Diversity, and Inclusion (JEDI) in Emerging Decision Support Technologies. Curr Oncol Rep 2023; 25:387-424. [PMID: 36811808 DOI: 10.1007/s11912-023-01376-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/06/2022] [Indexed: 02/24/2023]
Abstract
PURPOSE FOR REVIEW This perspective piece has two goals: first, to describe issues related to artificial intelligence-based applications for cancer control as they may impact health inequities or disparities; and second, to report on a review of systematic reviews and meta-analyses of artificial intelligence-based tools for cancer control to ascertain the extent to which discussions of justice, equity, diversity, inclusion, or health disparities manifest in syntheses of the field's best evidence. RECENT FINDINGS We found that, while a significant proportion of existing syntheses of research on AI-based tools in cancer control use formal bias assessment tools, the fairness or equitability of models is not yet systematically analyzable across studies. Issues related to real-world use of AI-based tools for cancer control, such as workflow considerations, measures of usability and acceptance, or tool architecture, are more visible in the literature, but still addressed only in a minority of reviews. Artificial intelligence is poised to bring significant benefits to a wide range of applications in cancer control, but more thorough and standardized evaluations and reporting of model fairness are required to build the evidence base for AI-based tool design for cancer and to ensure that these emerging technologies promote equitable healthcare.
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Affiliation(s)
- Peter Taber
- Department of Biomedical Informatics, University of Utah School of Medicine, 421 Wakara Way, Salt Lake City, UT, 84108, USA.
| | - Julie S Armin
- Department of Family and Community Medicine, University of Arizona College of Medicine, Tucson, AZ, USA
| | | | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah School of Medicine, 421 Wakara Way, Salt Lake City, UT, 84108, USA
| | - Jennifer Erdrich
- Division of Surgical Oncology, University of Arizona College of Medicine, Tucson, AZ, USA
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah School of Medicine, 421 Wakara Way, Salt Lake City, UT, 84108, USA
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Artificial intelligence in breast pathology - dawn of a new era. NPJ Breast Cancer 2023; 9:5. [PMID: 36720886 PMCID: PMC9889344 DOI: 10.1038/s41523-023-00507-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 01/10/2023] [Indexed: 02/02/2023] Open
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Enhanced Patient-Centricity: How the Biopharmaceutical Industry Is Optimizing Patient Care through AI/ML/DL. Healthcare (Basel) 2022; 10:healthcare10101997. [PMID: 36292444 PMCID: PMC9602573 DOI: 10.3390/healthcare10101997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 09/19/2022] [Accepted: 10/07/2022] [Indexed: 11/04/2022] Open
Abstract
Technologies utilizing cutting-edge methodologies, including artificial intelligence (AI), machine learning (ML) and deep learning (DL), present powerful opportunities to help evaluate, predict, and improve patient outcomes by drawing insights from real-world data (RWD) generated during medical care. They played a role during and following the Coronavirus Disease 2019 (COVID-19) pandemic by helping protect healthcare providers, prioritize care for vulnerable populations, predict disease trends, and find optimal therapies. Potential applications across therapeutic areas include diagnosis, disease management and patient journey mapping. Use of fit-for-purpose datasets for ML models is seeing growth and may potentially help additional enterprises develop AI strategies. However, biopharmaceutical companies often face specific challenges, including multi-setting data, system interoperability, data governance, and patient privacy requirements. There remains a need for evolving regulatory frameworks, operating models, and data governance to enable further developments and additional research. We explore recent literature and examine the hurdles faced by researchers in the biopharmaceutical industry to fully realize the promise of AI/ML/DL for patient-centric purposes.
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