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Bas TG. Bioactivity and Bioavailability of Carotenoids Applied in Human Health: Technological Advances and Innovation. Int J Mol Sci 2024; 25:7603. [PMID: 39062844 PMCID: PMC11277215 DOI: 10.3390/ijms25147603] [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: 05/10/2024] [Revised: 06/28/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024] Open
Abstract
This article presents a groundbreaking perspective on carotenoids, focusing on their innovative applications and transformative potential in human health and medicine. Research jointly delves deeper into the bioactivity and bioavailability of carotenoids, revealing therapeutic uses and technological advances that have the potential to revolutionize medical treatments. We explore pioneering therapeutic applications in which carotenoids are used to treat chronic diseases such as cancer, cardiovascular disease, and age-related macular degeneration, offering novel protective mechanisms and innovative therapeutic benefits. Our study also shows cutting-edge technological innovations in carotenoid extraction and bioavailability, including the development of supramolecular carriers and advanced nanotechnology, which dramatically improve the absorption and efficacy of these compounds. These technological advances not only ensure consistent quality but also tailor carotenoid therapies to each patient's health needs, paving the way for personalized medicine. By integrating the latest scientific discoveries and innovative techniques, this research provides a prospective perspective on the clinical applications of carotenoids, establishing a new benchmark for future studies in this field. Our findings underscore the importance of optimizing carotenoid extraction, administration, bioactivity, and bioavailability methods to develop more effective, targeted, and personalized treatments, thus offering visionary insight into their potential in modern medical practices.
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Affiliation(s)
- Tomas Gabriel Bas
- Escuela de Ciencias Empresariales, Universidad Catolica del Norte, Coquimbo 1780000, Chile
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2
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Georgakopoulou VE, Diamanti A. Artificial Intelligence for Smoking Cessation in Pregnancy. Cureus 2024; 16:e63732. [PMID: 39100043 PMCID: PMC11296692 DOI: 10.7759/cureus.63732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/03/2024] [Indexed: 08/06/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a revolutionary tool in various healthcare domains, including smoking cessation among pregnant women. Smoking during pregnancy is a significant public health concern, linked to adverse maternal and fetal outcomes. Traditional cessation methods have had limited success, necessitating innovative approaches. AI offers personalized interventions, predictive analytics, and real-time support, enhancing the effectiveness of smoking cessation programs. This editorial explores the potential of AI in transforming smoking cessation efforts for pregnant women, highlighting its benefits, challenges, and future prospects. By integrating AI into healthcare strategies, we can improve maternal and fetal health outcomes and contribute to the broader public health goal of reducing smoking rates among expectant mothers.
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Affiliation(s)
| | - Athina Diamanti
- Department of Midwifery, University of West Attica, Athens, GRC
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Dixon D, Sattar H, Moros N, Kesireddy SR, Ahsan H, Lakkimsetti M, Fatima M, Doshi D, Sadhu K, Junaid Hassan M. Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review. Cureus 2024; 16:e59954. [PMID: 38854327 PMCID: PMC11161909 DOI: 10.7759/cureus.59954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/08/2024] [Indexed: 06/11/2024] Open
Abstract
This comprehensive literature review explores the transformative impact of artificial intelligence (AI) predictive analytics on healthcare, particularly in improving patient outcomes regarding disease progression, treatment response, and recovery rates. AI, encompassing capabilities such as learning, problem-solving, and decision-making, is leveraged to predict disease progression, optimize treatment plans, and enhance recovery rates through the analysis of vast datasets, including electronic health records (EHRs), imaging, and genetic data. The utilization of machine learning (ML) and deep learning (DL) techniques in predictive analytics enables personalized medicine by facilitating the early detection of conditions, precision in drug discovery, and the tailoring of treatment to individual patient profiles. Ethical considerations, including data privacy, bias, and accountability, emerge as vital in the responsible implementation of AI in healthcare. The findings underscore the potential of AI predictive analytics in revolutionizing clinical decision-making and healthcare delivery, emphasizing the necessity of ethical guidelines and continuous model validation to ensure its safe and effective use in augmenting human judgment in medical practice.
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Affiliation(s)
- Diny Dixon
- Medicine, Jubilee Mission Medical College and Research Institute, Thrissur, IND
| | - Hina Sattar
- Medicine, Dow University of Health Sciences, Karachi, PAK
| | - Natalia Moros
- Medicine, Pontifical Javeriana University Medical School, Bogotá, COL
| | | | - Huma Ahsan
- Medicine, Jinnah Postgraduate Medical Centre, Karachi, PAK
| | | | - Madiha Fatima
- Medicine, Fatima Jinnah Medical University, Lahore, PAK
| | - Dhruvi Doshi
- Medicine, Gujarat Cancer Society Medical College, Hospital & Research Centre, Ahmedabad, IND
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4
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Li S, Xie J, Liu J, Wu Y, Wang Z, Cao Z, Wen D, Zhang X, Wang B, Yang Y, Lu L, Dong X. Prognostic Value of a Combined Nomogram Model Integrating 3-Dimensional Deep Learning and Radiomics for Head and Neck Cancer. J Comput Assist Tomogr 2024; 48:498-507. [PMID: 38438336 DOI: 10.1097/rct.0000000000001584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
OBJECTIVE The preoperative prediction of the overall survival (OS) status of patients with head and neck cancer (HNC) is significant value for their individualized treatment and prognosis. This study aims to evaluate the impact of adding 3D deep learning features to radiomics models for predicting 5-year OS status. METHODS Two hundred twenty cases from The Cancer Imaging Archive public dataset were included in this study; 2212 radiomics features and 304 deep features were extracted from each case. The features were selected by univariate analysis and the least absolute shrinkage and selection operator, and then grouped into a radiomics model containing Positron Emission Tomography /Computed Tomography (PET/CT) radiomics features score, a deep model containing deep features score, and a combined model containing PET/CT radiomics features score +3D deep features score. TumorStage model was also constructed using initial patient tumor node metastasis stage to compare the performance of the combined model. A nomogram was constructed to analyze the influence of deep features on the performance of the model. The 10-fold cross-validation of the average area under the receiver operating characteristic curve and calibration curve were used to evaluate performance, and Shapley Additive exPlanations (SHAP) was developed for interpretation. RESULTS The TumorStage model, radiomics model, deep model, and the combined model achieved areas under the receiver operating characteristic curve of 0.604, 0.851, 0.840, and 0.895 on the train set and 0.571, 0.849, 0.832, and 0.900 on the test set. The combined model showed better performance of predicting the 5-year OS status of HNC patients than the radiomics model and deep model. The combined model was shown to provide a favorable fit in calibration curves and be clinically useful in decision curve analysis. SHAP summary plot and SHAP The SHAP summary plot and SHAP force plot visually interpreted the influence of deep features and radiomics features on the model results. CONCLUSIONS In predicting 5-year OS status in patients with HNC, 3D deep features could provide richer features for combined model, which showed outperformance compared with the radiomics model and deep model.
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Affiliation(s)
| | - Jiayi Xie
- Department of automation, Tsinghua University, Beijing, China
| | | | | | - Zhongxiao Wang
- From the Hebei International Research Center for Medical-Engineering
| | - Zhendong Cao
- Department of Radiology, The Affiliated Hospital of Chengde Medical University, Chengde, Hebei
| | - Dong Wen
- Institute of Artificial Intelligence, University of Science and Technology Beijing
| | - Xiaolei Zhang
- From the Hebei International Research Center for Medical-Engineering
| | | | - Yifan Yang
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou
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5
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Delgado JL, Warren RC. To advance science we need to address 'otherness'. Nat Hum Behav 2024; 8:622-624. [PMID: 38308090 DOI: 10.1038/s41562-024-01821-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2024]
Affiliation(s)
- Jane L Delgado
- National Alliance for Hispanic Health, Washington, DC, USA.
- Healthy Americas Foundation, Washington, DC, USA.
| | - Rueben C Warren
- National Center for Bioethics in Research and Health Care, Tuskegee University, Tuskegee, AL, USA
- School of Dentistry, Meharry Medical College, Nashville, TN, USA
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6
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Qiao H, Chen Y, Qian C, Guo Y. Clinical data mining: challenges, opportunities, and recommendations for translational applications. J Transl Med 2024; 22:185. [PMID: 38378565 PMCID: PMC10880222 DOI: 10.1186/s12967-024-05005-0] [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: 12/07/2023] [Accepted: 02/18/2024] [Indexed: 02/22/2024] Open
Abstract
Clinical data mining of predictive models offers significant advantages for re-evaluating and leveraging large amounts of complex clinical real-world data and experimental comparison data for tasks such as risk stratification, diagnosis, classification, and survival prediction. However, its translational application is still limited. One challenge is that the proposed clinical requirements and data mining are not synchronized. Additionally, the exotic predictions of data mining are difficult to apply directly in local medical institutions. Hence, it is necessary to incisively review the translational application of clinical data mining, providing an analytical workflow for developing and validating prediction models to ensure the scientific validity of analytic workflows in response to clinical questions. This review systematically revisits the purpose, process, and principles of clinical data mining and discusses the key causes contributing to the detachment from practice and the misuse of model verification in developing predictive models for research. Based on this, we propose a niche-targeting framework of four principles: Clinical Contextual, Subgroup-Oriented, Confounder- and False Positive-Controlled (CSCF), to provide guidance for clinical data mining prior to the model's development in clinical settings. Eventually, it is hoped that this review can help guide future research and develop personalized predictive models to achieve the goal of discovering subgroups with varied remedial benefits or risks and ensuring that precision medicine can deliver its full potential.
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Affiliation(s)
- Huimin Qiao
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Yijing Chen
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, China
| | - Changshun Qian
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China
| | - You Guo
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, China.
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, China.
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China.
- Ganzhou Key Laboratory of Medical Big Data, Ganzhou, China.
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7
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Ciobanu-Caraus O, Aicher A, Kernbach JM, Regli L, Serra C, Staartjes VE. A critical moment in machine learning in medicine: on reproducible and interpretable learning. Acta Neurochir (Wien) 2024; 166:14. [PMID: 38227273 DOI: 10.1007/s00701-024-05892-8] [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: 11/21/2023] [Accepted: 12/14/2023] [Indexed: 01/17/2024]
Abstract
Over the past two decades, advances in computational power and data availability combined with increased accessibility to pre-trained models have led to an exponential rise in machine learning (ML) publications. While ML may have the potential to transform healthcare, this sharp increase in ML research output without focus on methodological rigor and standard reporting guidelines has fueled a reproducibility crisis. In addition, the rapidly growing complexity of these models compromises their interpretability, which currently impedes their successful and widespread clinical adoption. In medicine, where failure of such models may have severe implications for patients' health, the high requirements for accuracy, robustness, and interpretability confront ML researchers with a unique set of challenges. In this review, we discuss the semantics of reproducibility and interpretability, as well as related issues and challenges, and outline possible solutions to counteracting the "black box". To foster reproducibility, standard reporting guidelines need to be further developed and data or code sharing encouraged. Editors and reviewers may equally play a critical role by establishing high methodological standards and thus preventing the dissemination of low-quality ML publications. To foster interpretable learning, the use of simpler models more suitable for medical data can inform the clinician how results are generated based on input data. Model-agnostic explanation tools, sensitivity analysis, and hidden layer representations constitute further promising approaches to increase interpretability. Balancing model performance and interpretability are important to ensure clinical applicability. We have now reached a critical moment for ML in medicine, where addressing these issues and implementing appropriate solutions will be vital for the future evolution of the field.
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Affiliation(s)
- Olga Ciobanu-Caraus
- Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Anatol Aicher
- Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Julius M Kernbach
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Luca Regli
- Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Carlo Serra
- Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Victor E Staartjes
- Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
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Oniani D, Hilsman J, Peng Y, Poropatich RK, Pamplin JC, Legault GL, Wang Y. Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare. NPJ Digit Med 2023; 6:225. [PMID: 38042910 PMCID: PMC10693640 DOI: 10.1038/s41746-023-00965-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 11/15/2023] [Indexed: 12/04/2023] Open
Abstract
In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has garnered a lot of attention in the medical research community, leading to debates about its application in the healthcare sector, mainly due to concerns about transparency and related issues. Meanwhile, questions around the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied. As a result, there are no clear solutions to address ethical concerns, and decision-makers often neglect to consider the significance of ethical principles before implementing generative AI in clinical practice. In an attempt to address these issues, we explore ethical principles from the military perspective and propose the "GREAT PLEA" ethical principles, namely Governability, Reliability, Equity, Accountability, Traceability, Privacy, Lawfulness, Empathy, and Eutonomy, for generative AI in healthcare. Furthermore, we introduce a framework for adopting and expanding these ethical principles in a practical way that has been useful in the military and can be applied to healthcare for generative AI, based on contrasting their ethical concerns and risks. Ultimately, we aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI into healthcare practice.
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Affiliation(s)
- David Oniani
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jordan Hilsman
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yifan Peng
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Ronald K Poropatich
- Division of Pulmonary, Allergy, Critical Care & Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Military Medicine Research, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jeremy C Pamplin
- Telemedicine & Advanced Technology Research Center, US Army, Fort Detrick, Frederick, MD, USA
| | - Gary L Legault
- Department of Surgery, Uniformed Services University, Bethesda, MD, USA
- Virtual Medical Center, Brooke Army Medical Center, San Antonio, TX, USA
| | - Yanshan Wang
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA.
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA.
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
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9
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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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Petrik AF, Johnson ES, Mummadi R, Slaughter M, Coronado GD, Lin SC, Savitz L, Wallace N. The use of individual and multilevel data in the development of a risk prediction model to predict patients' likelihood of completing colorectal cancer screening. Prev Med Rep 2023; 36:102366. [PMID: 37732019 PMCID: PMC10507149 DOI: 10.1016/j.pmedr.2023.102366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 09/22/2023] Open
Abstract
Promotion of colorectal cancer (CRC) screening can be expensive and unnecessary for many patients. The use of predictive analytics promises to help health systems target the right services to the right patients at the right time while improving population health. Multilevel data at the interpersonal, organizational, community, and policy levels, is rarely considered in clinical decision making but may be used to improve CRC screening risk prediction. We compared the effectiveness of a CRC screening risk prediction model that uses multilevel data with a more conventional model that uses only individual patient data. We used a retrospective cohort to ascertain the one-year occurrence of CRC screening. The cohort was determined from a Health Maintenance Organization, in Oregon. Eligible patients were 50-75 years old, health plan members for at least one year before their birthday in 2018 and were due for screening. We created a risk model using logistic regression first with data available in the electronic health record (EHR), and then added multilevel data. In a cohort of 59,249 patients, 36.1% completed CRC screening. The individual level model included 14 demographic, clinical and encounter based characteristics, had a bootstrap-corrected C-statistic of 0.722 and sufficient calibration. The multilevel model added 9 variables from clinical setting and community characteristics, and the bootstrap-corrected C-statistic remained the same with continued sufficient calibration. The predictive power of the CRC screening model did not improve after adding multilevel data. Our findings suggest that multilevel data added no improvement to the prediction of the likelihood of CRC screening.
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Affiliation(s)
- Amanda F. Petrik
- Kaiser Permanente Center for Health Research, Portland, OR, USA
- Northwest Permanente, Portland, OR, USA
| | - Eric S. Johnson
- Kaiser Permanente Center for Health Research, Portland, OR, USA
- Northwest Permanente, Portland, OR, USA
| | - Rajasekhara Mummadi
- Kaiser Permanente Center for Health Research, Portland, OR, USA
- Northwest Permanente, Portland, OR, USA
| | | | | | - Sunny C. Lin
- Oregon Health & Science University/Portland State University School of Public Health, Portland, OR, USA
- Washington University, St. Louis, MO, USA
| | - Lucy Savitz
- Kaiser Permanente Center for Health Research, Portland, OR, USA
- University of Pittsburgh, Pittsburgh, PA, USA
| | - Neal Wallace
- Oregon Health & Science University/Portland State University School of Public Health, Portland, OR, USA
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11
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Motazedian P, Marbach JA, Prosperi-Porta G, Parlow S, Di Santo P, Abdel-Razek O, Jung R, Bradford WB, Tsang M, Hyon M, Pacifici S, Mohanty S, Ramirez FD, Huggins GS, Simard T, Hon S, Hibbert B. Diagnostic accuracy of point-of-care ultrasound with artificial intelligence-assisted assessment of left ventricular ejection fraction. NPJ Digit Med 2023; 6:201. [PMID: 37898711 PMCID: PMC10613290 DOI: 10.1038/s41746-023-00945-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 10/13/2023] [Indexed: 10/30/2023] Open
Abstract
Focused cardiac ultrasound (FoCUS) is becoming standard practice in a wide spectrum of clinical settings. There is limited data evaluating the real-world use of FoCUS with artificial intelligence (AI). Our objective was to determine the accuracy of FoCUS AI-assisted left ventricular ejection fraction (LVEF) assessment and compare its accuracy between novice and experienced users. In this prospective, multicentre study, participants requiring a transthoracic echocardiogram (TTE) were recruited to have a FoCUS done by a novice or experienced user. The AI-assisted device calculated LVEF at the bedside, which was subsequently compared to TTE. 449 participants were enrolled with 424 studies included in the final analysis. The overall intraclass coefficient was 0.904, and 0.921 in the novice (n = 208) and 0.845 in the experienced (n = 216) cohorts. There was a significant bias of 0.73% towards TTE (p = 0.005) with a level of agreement of 11.2%. Categorical grading of LVEF severity had excellent agreement to TTE (weighted kappa = 0.83). The area under the curve (AUC) was 0.98 for identifying an abnormal LVEF (<50%) with a sensitivity of 92.8%, specificity of 92.3%, negative predictive value (NPV) of 0.97 and a positive predictive value (PPV) of 0.83. In identifying severe dysfunction (<30%) the AUC was 0.99 with a sensitivity of 78.1%, specificity of 98.0%, NPV of 0.98 and PPV of 0.76. Here we report that FoCUS AI-assisted LVEF assessments provide highly reproducible LVEF estimations in comparison to formal TTE. This finding was consistent among senior and novice echocardiographers suggesting applicability in a variety of clinical settings.
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Affiliation(s)
- Pouya Motazedian
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | - Jeffrey A Marbach
- Division of Cardiology, Knight Cardiovascular Institute, Oregon Health and Sciences University, Portland, OR, USA
| | - Graeme Prosperi-Porta
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Simon Parlow
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Pietro Di Santo
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
- Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Omar Abdel-Razek
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Richard Jung
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
- Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - William B Bradford
- Division of Cardiology, Tufts Medical Center and Tufts University School of Medicine, Boston, MA, USA
| | - Miranda Tsang
- Division of Cardiology, Tufts Medical Center and Tufts University School of Medicine, Boston, MA, USA
| | - Michael Hyon
- Division of Cardiology, Tufts Medical Center and Tufts University School of Medicine, Boston, MA, USA
| | - Stefano Pacifici
- Division of Cardiology, Tufts Medical Center and Tufts University School of Medicine, Boston, MA, USA
| | - Sharanya Mohanty
- Division of Cardiology, Tufts Medical Center and Tufts University School of Medicine, Boston, MA, USA
| | - F Daniel Ramirez
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Gordon S Huggins
- Division of Cardiology, Tufts Medical Center and Tufts University School of Medicine, Boston, MA, USA
| | - Trevor Simard
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Stephanie Hon
- Division of Pulmonary and Critical Care Medicine, Tufts Medical Center and Tufts University School of Medicine, Boston, MA, USA
| | - Benjamin Hibbert
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
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Baumgartner R, Arora P, Bath C, Burljaev D, Ciereszko K, Custers B, Ding J, Ernst W, Fosch-Villaronga E, Galanos V, Gremsl T, Hendl T, Kropp C, Lenk C, Martin P, Mbelu S, Morais Dos Santos Bruss S, Napiwodzka K, Nowak E, Roxanne T, Samerski S, Schneeberger D, Tampe-Mai K, Vlantoni K, Wiggert K, Williams R. Fair and equitable AI in biomedical research and healthcare: Social science perspectives. Artif Intell Med 2023; 144:102658. [PMID: 37783540 DOI: 10.1016/j.artmed.2023.102658] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 06/30/2023] [Accepted: 09/01/2023] [Indexed: 10/04/2023]
Abstract
Artificial intelligence (AI) offers opportunities but also challenges for biomedical research and healthcare. This position paper shares the results of the international conference "Fair medicine and AI" (online 3-5 March 2021). Scholars from science and technology studies (STS), gender studies, and ethics of science and technology formulated opportunities, challenges, and research and development desiderata for AI in healthcare. AI systems and solutions, which are being rapidly developed and applied, may have undesirable and unintended consequences including the risk of perpetuating health inequalities for marginalized groups. Socially robust development and implications of AI in healthcare require urgent investigation. There is a particular dearth of studies in human-AI interaction and how this may best be configured to dependably deliver safe, effective and equitable healthcare. To address these challenges, we need to establish diverse and interdisciplinary teams equipped to develop and apply medical AI in a fair, accountable and transparent manner. We formulate the importance of including social science perspectives in the development of intersectionally beneficent and equitable AI for biomedical research and healthcare, in part by strengthening AI health evaluation.
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Affiliation(s)
- Renate Baumgartner
- Center of Gender- and Diversity Research, University of Tübingen, Wilhelmstrasse 56, 72074 Tübingen, Germany; Athena Institute, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands.
| | - Payal Arora
- Erasmus School of Philosophy, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA Rotterdam, The Netherlands
| | - Corinna Bath
- Gender, Technology and Mobility, Institute for Flight Guidance, TU Braunschweig, Hermann-Blenk-Str. 27, 38108 Braunschweig, Germany
| | - Darja Burljaev
- Center of Gender- and Diversity Research, University of Tübingen, Wilhelmstrasse 56, 72074 Tübingen, Germany
| | - Kinga Ciereszko
- Department of Philosophy, Adam Mickiewicz University in Poznan, Szamarzewski Street 89C, 60-569 Poznan, Poland
| | - Bart Custers
- eLaw - Center for Law and Digital Technologies, Leiden University, Steenschuur 25, 2311 ES Leiden, Netherlands
| | - Jin Ding
- iHuman and Department of Sociological Studies, University of Sheffield, ICOSS, 219 Portobello, Sheffield S1 4DP, United Kingdom
| | - Waltraud Ernst
- Institute for Women's and Gender Studies, Johannes Kepler University Linz, Altenberger Strasse 69, 4040 Linz, Austria
| | - Eduard Fosch-Villaronga
- eLaw - Center for Law and Digital Technologies, Leiden University, Steenschuur 25, 2311 ES Leiden, Netherlands
| | - Vassilis Galanos
- Science, Technology and Innovation Studies, School of Social and Political Science, University of Edinburgh, Old Surgeons' Hall, High School Yards, Edinburgh EH1 1LZ, United Kingdom
| | - Thomas Gremsl
- Institute of Ethics and Social Teaching, Faculty of Catholic Theology, University of Graz, Heinrichstraße 78b/2, 8010 Graz, Austria
| | - Tereza Hendl
- Professorship for Ethics of Medicine, University of Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; Institute of Ethics, History and Theory of Medicine, Ludwig-Maximilians-University in Munich, Lessingstr. 2, 80336 Munich, Germany
| | - Cordula Kropp
- Center for Interdisciplinary Risk and Innovation Studies (ZIRIUS), University of Stuttgart, Seidenstraße 36, 70174 Stuttgart, Germany
| | - Christian Lenk
- Institute of the History, Philosophy and Ethics of Medicine, Ulm University, Parkstraße 11, 89073 Ulm, Germany
| | - Paul Martin
- iHuman and Department of Sociological Studies, University of Sheffield, ICOSS, 219 Portobello, Sheffield S1 4DP, United Kingdom
| | - Somto Mbelu
- Erasmus School of Philosophy, Erasmus University Rotterdam, 10A Ademola Close off Remi Fani Kayode Street, GRA Ikeja, Lagos, Nigeria
| | | | - Karolina Napiwodzka
- Department of Philosophy, Adam Mickiewicz University in Poznan, Szamarzewski Street 89C, 60-569 Poznan, Poland
| | - Ewa Nowak
- Department of Philosophy, Adam Mickiewicz University in Poznan, Szamarzewski Street 89C, 60-569 Poznan, Poland
| | - Tiara Roxanne
- Data & Society Institute, 228 Park Ave S PMB 83075, New York, NY 10003-1502, United States of America
| | - Silja Samerski
- Fachbereich Soziale Arbeit und Gesundheit, Hochschule Emden/Leer, Constantiaplatz 4, 26723 Emden, Germany
| | - David Schneeberger
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Auenbruggerplatz 2, 8036 Graz, Austria
| | - Karolin Tampe-Mai
- Center for Interdisciplinary Risk and Innovation Studies (ZIRIUS), University of Stuttgart, Seidenstraße 36, 70174 Stuttgart, Germany
| | - Katerina Vlantoni
- Department of History and Philosophy of Science, School of Science, National and Kapodistrian University of Athens, Panepistimioupoli, Ilisia, Athens 15771, Greece
| | - Kevin Wiggert
- Institute of Sociology, Department Sociology of Technology and Innovation, Technical University of Berlin, Fraunhoferstraße 33-36, 10623 Berlin, Germany
| | - Robin Williams
- Science, Technology and Innovation Studies, School of Social and Political Science, University of Edinburgh, Old Surgeons' Hall, High School Yards, Edinburgh EH1 1LZ, United Kingdom
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Cui S, Traverso A, Niraula D, Zou J, Luo Y, Owen D, El Naqa I, Wei L. Interpretable artificial intelligence in radiology and radiation oncology. Br J Radiol 2023; 96:20230142. [PMID: 37493248 PMCID: PMC10546466 DOI: 10.1259/bjr.20230142] [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: 02/11/2023] [Revised: 05/24/2023] [Accepted: 05/26/2023] [Indexed: 07/27/2023] Open
Abstract
Artificial intelligence has been introduced to clinical practice, especially radiology and radiation oncology, from image segmentation, diagnosis, treatment planning and prognosis. It is not only crucial to have an accurate artificial intelligence model, but also to understand the internal logic and gain the trust of the experts. This review is intended to provide some insights into core concepts of the interpretability, the state-of-the-art methods for understanding the machine learning models, the evaluation of these methods, identifying some challenges and limits of them, and gives some examples of medical applications.
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Affiliation(s)
- Sunan Cui
- Department of Radiation Oncology, University of Washington, Seattle, WA, United States
| | - Alberto Traverso
- Department of Radiotherapy, Maastro Clinic, Maastricht, Netherlands
| | - Dipesh Niraula
- Department of Machine Learning, Moffitt Cancer Center, FL, United States
| | - Jiaren Zou
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Yi Luo
- Department of Machine Learning, Moffitt Cancer Center, FL, United States
| | - Dawn Owen
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, United States
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, FL, United States
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
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Affiliation(s)
- David J Hunter
- From the Nuffield Department of Population Health (D.J.H.) and the Department of Statistics and Nuffield Department of Medicine (C.H.), University of Oxford, Oxford, and the Alan Turing Institute, London (C.H.) - both in the United Kingdom
| | - Christopher Holmes
- From the Nuffield Department of Population Health (D.J.H.) and the Department of Statistics and Nuffield Department of Medicine (C.H.), University of Oxford, Oxford, and the Alan Turing Institute, London (C.H.) - both in the United Kingdom
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15
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Thaxton C, Dardik A. Computer Science meets Vascular Surgery: Keeping a pulse on artificial intelligence. Semin Vasc Surg 2023; 36:419-425. [PMID: 37863614 PMCID: PMC10589450 DOI: 10.1053/j.semvascsurg.2023.05.003] [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: 03/27/2023] [Revised: 05/01/2023] [Accepted: 05/24/2023] [Indexed: 10/22/2023]
Abstract
Artificial intelligence (AI)-based technologies have garnered interest across a range of disciplines in the past several years, with an even more recent interest in various health care fields, including Vascular Surgery. AI offers a unique ability to analyze health data more quickly and efficiently than could be done by humans alone and can be used for clinical applications such as diagnosis, risk stratification, and follow-up, as well as patient-used applications to improve both patient and provider experiences, mitigate health care disparities, and individualize treatment. As with all novel technologies, AI is not without its risks and carries with it unique ethical considerations that will need to be addressed before its broad integration into health care systems. AI has the potential to revolutionize the way care is provided to patients, including those requiring vascular care.
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Affiliation(s)
- Carly Thaxton
- Department of Surgery, Yale School of Medicine, 10 Amistad Street, Room 437, New Haven, CT 06519; The Vascular Biology and Therapeutics Program, Yale School of Medicine, New Haven, CT
| | - Alan Dardik
- Department of Surgery, Yale School of Medicine, 10 Amistad Street, Room 437, New Haven, CT 06519; The Vascular Biology and Therapeutics Program, Yale School of Medicine, New Haven, CT; Department of Cellular and Molecular Physiology, Yale School of Medicine, New Haven, CT.
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16
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Novak LL, Russell RG, Garvey K, Patel M, Thomas Craig KJ, Snowdon J, Miller B. Clinical use of artificial intelligence requires AI-capable organizations. JAMIA Open 2023; 6:ooad028. [PMID: 37152469 PMCID: PMC10155810 DOI: 10.1093/jamiaopen/ooad028] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 03/18/2023] [Accepted: 04/11/2023] [Indexed: 05/09/2023] Open
Abstract
Artificial intelligence-based algorithms are being widely implemented in health care, even as evidence is emerging of bias in their design, problems with implementation, and potential harm to patients. To achieve the promise of using of AI-based tools to improve health, healthcare organizations will need to be AI-capable, with internal and external systems functioning in tandem to ensure the safe, ethical, and effective use of AI-based tools. Ideas are starting to emerge about the organizational routines, competencies, resources, and infrastructures that will be required for safe and effective deployment of AI in health care, but there has been little empirical research. Infrastructures that provide legal and regulatory guidance for managers, clinician competencies for the safe and effective use of AI-based tools, and learner-centric resources such as clear AI documentation and local health ecosystem impact reviews can help drive continuous improvement.
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Affiliation(s)
- Laurie Lovett Novak
- Corresponding Author: Laurie Lovett Novak, PhD, MHSA, Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Ave, Suite 1475, Nashville, TN 37203, USA;
| | - Regina G Russell
- Department of Medical Education and Administration and Office of Undergraduate Medical Education, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Kim Garvey
- Department of Anesthesiology and the Center for Advanced Mobile Healthcare Learning, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Mehool Patel
- Department of Internal Medicine, Northeastern Ohio Medical University (NEOMED), Rootstown, Ohio, USA
- Department of Internal Medicine, Western Reserve Hospital, Cuyahoga Falls, Ohio, USA
| | - Kelly Jean Thomas Craig
- Clinical Evidence Development, Aetna®, Medical Affairs CVS Health®, Wellesley, Massachusetts, USA
| | - Jane Snowdon
- Corporate Technical Strategy, IBM® Corporation, Yorktown Heights, New York, USA
| | - Bonnie Miller
- Department of Medical Education and Administration and Center for Advanced Mobile Healthcare Learning, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Brizzi F, Steiert B, Pang H, Diack C, Lomax M, Peck R, Morgan Z, Soubret A. A model-based approach for historical borrowing, with an application to neovascular age-related macular degeneration. Stat Methods Med Res 2023; 32:1064-1081. [PMID: 37082812 DOI: 10.1177/09622802231155597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Abstract
Bayesian historical borrowing has recently attracted growing interest due to the increasing availability of historical control data, as well as improved computational methodology and software. In this article, we argue that the statistical models used for borrowing may be suboptimal when they do not adjust for differing factors across historical studies such as covariates, dosing regimen, etc. We propose an alternative approach to address these shortcomings. We start by constructing a historical model based on subject-level historical data to accurately characterize the control treatment by adjusting for known between trials differences. This model is subsequently used to predict the control arm response in the current trial, enabling the derivation of a model-informed prior for the treatment effect parameter of another (potentially simpler) model used to analyze the trial efficacy (i.e. the trial model). Our approach is applied to neovascular age-related macular degeneration trials, employing a cross-sectional regression trial model, and a longitudinal non-linear mixed-effects drug-disease-trial historical model. The latter model characterizes the relationship between clinical response, drug exposure and baseline covariates so that the derived model-informed prior seamlessly adapts to the trial population and can be extrapolated to a different dosing regimen. This approach can yield a more accurate prior for borrowing, thus optimizing gains in efficiency (e.g. increasing power or reducing the sample size) in future trials.
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Affiliation(s)
- Francesco Brizzi
- Predictive Modelling and Data Analytics, Roche Pharma Research & Early Development, Roche Innovation Center Basel, Switzerland
| | - Bernhard Steiert
- Predictive Modelling and Data Analytics, Roche Pharma Research & Early Development, Roche Innovation Center Basel, Switzerland
| | - Herbert Pang
- Methods Collaboration & Outreach (MCO) Enabling Platform, Genentech Inc., South San Francisco, USA
| | - Cheikh Diack
- Predictive Modelling and Data Analytics, Roche Pharma Research & Early Development, Roche Innovation Center Basel, Switzerland
| | - Mark Lomax
- Data & Statistical Sciences, F. Hoffman-La Roche Ltd, Welwyn Garden City, UK
| | - Robbie Peck
- Data & Statistical Sciences, Hoffmann-La Roche AG, Basel, Switzerland
| | - Zoe Morgan
- Data & Statistical Sciences, Hoffmann-La Roche AG, Basel, Switzerland
| | - Antoine Soubret
- Predictive Modelling and Data Analytics, Roche Pharma Research & Early Development, Roche Innovation Center Basel, Switzerland
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Park SH, Han K, Jang HY, Park JE, Lee JG, Kim DW, Choi J. Methods for Clinical Evaluation of Artificial Intelligence Algorithms for Medical Diagnosis. Radiology 2023; 306:20-31. [PMID: 36346314 DOI: 10.1148/radiol.220182] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Adequate clinical evaluation of artificial intelligence (AI) algorithms before adoption in practice is critical. Clinical evaluation aims to confirm acceptable AI performance through adequate external testing and confirm the benefits of AI-assisted care compared with conventional care through appropriately designed and conducted studies, for which prospective studies are desirable. This article explains some of the fundamental methodological points that should be considered when designing and appraising the clinical evaluation of AI algorithms for medical diagnosis. The specific topics addressed include the following: (a) the importance of external testing of AI algorithms and strategies for conducting the external testing effectively, (b) the various metrics and graphical methods for evaluating the AI performance as well as essential methodological points to note in using and interpreting them, (c) paired study designs primarily for comparative performance evaluation of conventional and AI-assisted diagnoses, (d) parallel study designs primarily for evaluating the effect of AI intervention with an emphasis on randomized clinical trials, and (e) up-to-date guidelines for reporting clinical studies on AI, with an emphasis on guidelines registered in the EQUATOR Network library. Sound methodological knowledge of these topics will aid the design, execution, reporting, and appraisal of clinical evaluation of AI.
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Affiliation(s)
- Seong Ho Park
- From the Department of Radiology and Research Institute of Radiology (S.H.P., J.E.P., D.W.K.) and Department of Biomedical Engineering (J.C.), Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea (K.H.); Department of Radiology, National Cancer Center, Goyang, South Korea (H.Y.J.); and Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea (J.G.L.)
| | - Kyunghwa Han
- From the Department of Radiology and Research Institute of Radiology (S.H.P., J.E.P., D.W.K.) and Department of Biomedical Engineering (J.C.), Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea (K.H.); Department of Radiology, National Cancer Center, Goyang, South Korea (H.Y.J.); and Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea (J.G.L.)
| | - Hye Young Jang
- From the Department of Radiology and Research Institute of Radiology (S.H.P., J.E.P., D.W.K.) and Department of Biomedical Engineering (J.C.), Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea (K.H.); Department of Radiology, National Cancer Center, Goyang, South Korea (H.Y.J.); and Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea (J.G.L.)
| | - Ji Eun Park
- From the Department of Radiology and Research Institute of Radiology (S.H.P., J.E.P., D.W.K.) and Department of Biomedical Engineering (J.C.), Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea (K.H.); Department of Radiology, National Cancer Center, Goyang, South Korea (H.Y.J.); and Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea (J.G.L.)
| | - June-Goo Lee
- From the Department of Radiology and Research Institute of Radiology (S.H.P., J.E.P., D.W.K.) and Department of Biomedical Engineering (J.C.), Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea (K.H.); Department of Radiology, National Cancer Center, Goyang, South Korea (H.Y.J.); and Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea (J.G.L.)
| | - Dong Wook Kim
- From the Department of Radiology and Research Institute of Radiology (S.H.P., J.E.P., D.W.K.) and Department of Biomedical Engineering (J.C.), Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea (K.H.); Department of Radiology, National Cancer Center, Goyang, South Korea (H.Y.J.); and Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea (J.G.L.)
| | - Jaesoon Choi
- From the Department of Radiology and Research Institute of Radiology (S.H.P., J.E.P., D.W.K.) and Department of Biomedical Engineering (J.C.), Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea (K.H.); Department of Radiology, National Cancer Center, Goyang, South Korea (H.Y.J.); and Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea (J.G.L.)
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Shan Y, Ji M, Xie W, Lam KY, Chow CY. Public Trust in Artificial Intelligence Applications in Mental Health Care: Topic Modeling Analysis. JMIR Hum Factors 2022; 9:e38799. [DOI: 10.2196/38799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 07/10/2022] [Accepted: 11/09/2022] [Indexed: 12/05/2022] Open
Abstract
Background
Mental disorders (MDs) impose heavy burdens on health care (HC) systems and affect a growing number of people worldwide. The use of mobile health (mHealth) apps empowered by artificial intelligence (AI) is increasingly being resorted to as a possible solution.
Objective
This study adopted a topic modeling (TM) approach to investigate the public trust in AI apps in mental health care (MHC) by identifying the dominant topics and themes in user reviews of the 8 most relevant mental health (MH) apps with the largest numbers of reviewers.
Methods
We searched Google Play for the top MH apps with the largest numbers of reviewers, from which we selected the most relevant apps. Subsequently, we extracted data from user reviews posted from January 1, 2020, to April 2, 2022. After cleaning the extracted data using the Python text processing tool spaCy, we ascertained the optimal number of topics, drawing on the coherence scores and used latent Dirichlet allocation (LDA) TM to generate the most salient topics and related terms. We then classified the ascertained topics into different theme categories by plotting them onto a 2D plane via multidimensional scaling using the pyLDAvis visualization tool. Finally, we analyzed these topics and themes qualitatively to better understand the status of public trust in AI apps in MHC.
Results
From the top 20 MH apps with the largest numbers of reviewers retrieved, we chose the 8 (40%) most relevant apps: (1) Wysa: Anxiety Therapy Chatbot; (2) Youper Therapy; (3) MindDoc: Your Companion; (4) TalkLife for Anxiety, Depression & Stress; (5) 7 Cups: Online Therapy for Mental Health & Anxiety; (6) BetterHelp-Therapy; (7) Sanvello; and (8) InnerHour. These apps provided 14.2% (n=559), 11.0% (n=431), 13.7% (n=538), 8.8% (n=356), 14.1% (n=554), 11.9% (n=468), 9.2% (n=362), and 16.9% (n=663) of the collected 3931 reviews, respectively. The 4 dominant topics were topic 4 (cheering people up; n=1069, 27%), topic 3 (calming people down; n=1029, 26%), topic 2 (helping figure out the inner world; n=963, 25%), and topic 1 (being an alternative or complement to a therapist; n=870, 22%). Based on topic coherence and intertopic distance, topics 3 and 4 were combined into theme 3 (dispelling negative emotions), while topics 2 and 1 remained 2 separate themes: theme 2 (helping figure out the inner world) and theme 1 (being an alternative or complement to a therapist), respectively. These themes and topics, though involving some dissenting voices, reflected an overall high status of trust in AI apps.
Conclusions
This is the first study to investigate the public trust in AI apps in MHC from the perspective of user reviews using the TM technique. The automatic text analysis and complementary manual interpretation of the collected data allowed us to discover the dominant topics hidden in a data set and categorize these topics into different themes to reveal an overall high degree of public trust. The dissenting voices from users, though only a few, can serve as indicators for health providers and app developers to jointly improve these apps, which will ultimately facilitate the treatment of prevalent MDs and alleviate the overburdened HC systems worldwide.
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Toward safe AI. AI & SOCIETY 2022. [DOI: 10.1007/s00146-022-01591-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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21
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Mainz JT, Sønderholm J, Uhrenfeldt R. Artificial intelligence and the secret ballot. AI & SOCIETY 2022. [DOI: 10.1007/s00146-022-01551-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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22
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Choudhury A. Factors influencing clinicians' willingness to use an AI-based clinical decision support system. Front Digit Health 2022; 4:920662. [PMID: 36339516 PMCID: PMC9628998 DOI: 10.3389/fdgth.2022.920662] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 08/01/2022] [Indexed: 11/07/2022] Open
Abstract
Background Given the opportunities created by artificial intelligence (AI) based decision support systems in healthcare, the vital question is whether clinicians are willing to use this technology as an integral part of clinical workflow. Purpose This study leverages validated questions to formulate an online survey and consequently explore cognitive human factors influencing clinicians' intention to use an AI-based Blood Utilization Calculator (BUC), an AI system embedded in the electronic health record that delivers data-driven personalized recommendations for the number of packed red blood cells to transfuse for a given patient. Method A purposeful sampling strategy was used to exclusively include BUC users who are clinicians in a university hospital in Wisconsin. We recruited 119 BUC users who completed the entire survey. We leveraged structural equation modeling to capture the direct and indirect effects of “AI Perception” and “Expectancy” on clinicians' Intention to use the technology when mediated by “Perceived Risk”. Results The findings indicate a significant negative relationship concerning the direct impact of AI's perception on BUC Risk (ß = −0.23, p < 0.001). Similarly, Expectancy had a significant negative effect on Risk (ß = −0.49, p < 0.001). We also noted a significant negative impact of Risk on the Intent to use BUC (ß = −0.34, p < 0.001). Regarding the indirect effect of Expectancy on the Intent to Use BUC, the findings show a significant positive impact mediated by Risk (ß = 0.17, p = 0.004). The study noted a significant positive and indirect effect of AI Perception on the Intent to Use BUC when mediated by risk (ß = 0.08, p = 0.027). Overall, this study demonstrated the influences of expectancy, perceived risk, and perception of AI on clinicians' intent to use BUC (an AI system). AI developers need to emphasize the benefits of AI technology, ensure ease of use (effort expectancy), clarify the system's potential (performance expectancy), and minimize the risk perceptions by improving the overall design. Conclusion Identifying the factors that determine clinicians' intent to use AI-based decision support systems can help improve technology adoption and use in the healthcare domain. Enhanced and safe adoption of AI can uplift the overall care process and help standardize clinical decisions and procedures. An improved AI adoption in healthcare will help clinicians share their everyday clinical workload and make critical decisions.
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Parikh RB, Basen-Enquist KM, Bradley C, Estrin D, Levy M, Lichtenfeld JL, Malin B, McGraw D, Meropol NJ, Oyer RA, Sheldon LK, Shulman LN. Digital Health Applications in Oncology: An Opportunity to Seize. J Natl Cancer Inst 2022; 114:1338-1339. [PMID: 35640986 PMCID: PMC9384132 DOI: 10.1093/jnci/djac108] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 04/13/2022] [Accepted: 05/03/2022] [Indexed: 11/23/2022] Open
Abstract
Digital health advances have transformed many clinical areas including psychiatric and cardiovascular care. However, digital health innovation is relatively nascent in cancer care, which represents the fastest growing area of health-care spending. Opportunities for digital health innovation in oncology include patient-facing technologies that improve patient experience, safety, and patient-clinician interactions; clinician-facing technologies that improve their ability to diagnose pathology and predict adverse events; and quality of care and research infrastructure to improve clinical workflows, documentation, decision support, and clinical trial monitoring. The COVID-19 pandemic and associated shifts of care to the home and community dramatically accelerated the integration of digital health technologies into virtually every aspect of oncology care. However, the pandemic has also exposed potential flaws in the digital health ecosystem, namely in clinical integration strategies; data access, quality, and security; and regulatory oversight and reimbursement for digital health technologies. Stemming from the proceedings of a 2020 workshop convened by the National Cancer Policy Forum of the National Academies of Sciences, Engineering, and Medicine, this article summarizes the current state of digital health technologies in medical practice and strategies to improve clinical utility and integration. These recommendations, with calls to action for clinicians, health systems, technology innovators, and policy makers, will facilitate efficient yet safe integration of digital health technologies into cancer care.
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Affiliation(s)
- Ravi B Parikh
- Division of Hematology Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Center for Health Equity Research and Promotion, Corporal Michael J. Crescenz VAMC, Philadelphia, Pennsylvania, USA
| | - Karen M Basen-Enquist
- Center for Energy Balance in Cancer Prevention and Survivorship, The University of Texas MD Anderson Cancer Center, Texas Medical Center, Houston, Texas, USA
| | - Cathy Bradley
- University of Colorado Cancer Center, Aurora, Colorado, USA
| | | | - Mia Levy
- Division of Hematology, Oncology and Cell Therapy, Rush University, Chicago, Illinois, USA
| | | | - Bradley Malin
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | | | - Randall A Oyer
- Ann B. Barshinger Cancer Institute, Lancaster, Pennsylvania, USA
| | - Lisa Kennedy Sheldon
- College of Nursing and Health Sciences, University of Massachusetts, Boston, Massachusetts, USA
| | - Lawrence N Shulman
- Division of Hematology Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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24
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Gulati G, Upshaw J, Wessler BS, Brazil RJ, Nelson J, van Klaveren D, Lundquist CM, Park JG, McGinnes H, Steyerberg EW, Van Calster B, Kent DM. Generalizability of Cardiovascular Disease Clinical Prediction Models: 158 Independent External Validations of 104 Unique Models. Circ Cardiovasc Qual Outcomes 2022; 15:e008487. [PMID: 35354282 PMCID: PMC9015037 DOI: 10.1161/circoutcomes.121.008487] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background: While clinical prediction models (CPMs) are used increasingly commonly to guide patient care, the performance and clinical utility of these CPMs in new patient cohorts is poorly understood. Methods: We performed 158 external validations of 104 unique CPMs across 3 domains of cardiovascular disease (primary prevention, acute coronary syndrome, and heart failure). Validations were performed in publicly available clinical trial cohorts and model performance was assessed using measures of discrimination, calibration, and net benefit. To explore potential reasons for poor model performance, CPM-clinical trial cohort pairs were stratified based on relatedness, a domain-specific set of characteristics to qualitatively grade the similarity of derivation and validation patient populations. We also examined the model-based C-statistic to assess whether changes in discrimination were because of differences in case-mix between the derivation and validation samples. The impact of model updating on model performance was also assessed. Results: Discrimination decreased significantly between model derivation (0.76 [interquartile range 0.73–0.78]) and validation (0.64 [interquartile range 0.60–0.67], P<0.001), but approximately half of this decrease was because of narrower case-mix in the validation samples. CPMs had better discrimination when tested in related compared with distantly related trial cohorts. Calibration slope was also significantly higher in related trial cohorts (0.77 [interquartile range, 0.59–0.90]) than distantly related cohorts (0.59 [interquartile range 0.43–0.73], P=0.001). When considering the full range of possible decision thresholds between half and twice the outcome incidence, 91% of models had a risk of harm (net benefit below default strategy) at some threshold; this risk could be reduced substantially via updating model intercept, calibration slope, or complete re-estimation. Conclusions: There are significant decreases in model performance when applying cardiovascular disease CPMs to new patient populations, resulting in substantial risk of harm. Model updating can mitigate these risks. Care should be taken when using CPMs to guide clinical decision-making.
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Affiliation(s)
- Gaurav Gulati
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center, Boston, MA (G.G., J.U., B.S.W., R.J.B., J.N., D.v.K., C.M.L., J.G.P., H.M., D.M.K.).,Division of Cardiology, Tufts Medical Center, Boston, MA (G.G., J.U., B.S.W.)
| | - Jenica Upshaw
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center, Boston, MA (G.G., J.U., B.S.W., R.J.B., J.N., D.v.K., C.M.L., J.G.P., H.M., D.M.K.).,Division of Cardiology, Tufts Medical Center, Boston, MA (G.G., J.U., B.S.W.)
| | - Benjamin S Wessler
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center, Boston, MA (G.G., J.U., B.S.W., R.J.B., J.N., D.v.K., C.M.L., J.G.P., H.M., D.M.K.).,Division of Cardiology, Tufts Medical Center, Boston, MA (G.G., J.U., B.S.W.)
| | - Riley J Brazil
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center, Boston, MA (G.G., J.U., B.S.W., R.J.B., J.N., D.v.K., C.M.L., J.G.P., H.M., D.M.K.)
| | - Jason Nelson
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center, Boston, MA (G.G., J.U., B.S.W., R.J.B., J.N., D.v.K., C.M.L., J.G.P., H.M., D.M.K.)
| | - David van Klaveren
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center, Boston, MA (G.G., J.U., B.S.W., R.J.B., J.N., D.v.K., C.M.L., J.G.P., H.M., D.M.K.).,Department of Biomedical Data Sciences, Leiden University Medical Centre, Netherlands (D.v.K., E.W.S., B.V.C.)
| | - Christine M Lundquist
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center, Boston, MA (G.G., J.U., B.S.W., R.J.B., J.N., D.v.K., C.M.L., J.G.P., H.M., D.M.K.)
| | - Jinny G Park
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center, Boston, MA (G.G., J.U., B.S.W., R.J.B., J.N., D.v.K., C.M.L., J.G.P., H.M., D.M.K.)
| | - Hannah McGinnes
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center, Boston, MA (G.G., J.U., B.S.W., R.J.B., J.N., D.v.K., C.M.L., J.G.P., H.M., D.M.K.)
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Netherlands (D.v.K., E.W.S., B.V.C.)
| | - Ben Van Calster
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Netherlands (D.v.K., E.W.S., B.V.C.).,KU Leuven, Department of Development and Regeneration, Belgium (B.V.C.).,EPI-Center, KU Leuven, Belgium (B.V.C.)
| | - David M Kent
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center, Boston, MA (G.G., J.U., B.S.W., R.J.B., J.N., D.v.K., C.M.L., J.G.P., H.M., D.M.K.)
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25
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Kim EY, Kim YJ, Choi WJ, Jeon JS, Kim MY, Oh DH, Jin KN, Cho YJ. Concordance rate of radiologists and a commercialized deep-learning solution for chest X-ray: Real-world experience with a multicenter health screening cohort. PLoS One 2022; 17:e0264383. [PMID: 35202417 PMCID: PMC8870572 DOI: 10.1371/journal.pone.0264383] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 02/09/2022] [Indexed: 11/18/2022] Open
Abstract
Purpose Lunit INSIGHT CXR (Lunit) is a commercially available deep-learning algorithm-based decision support system for chest radiography (CXR). This retrospective study aimed to evaluate the concordance rate of radiologists and Lunit for thoracic abnormalities in a multicenter health screening cohort. Methods and materials We retrospectively evaluated the radiology reports and Lunit results for CXR at several health screening centers in August 2020. Lunit was adopted as a clinical decision support system (CDSS) in routine clinical practice. Subsequently, radiologists completed their reports after reviewing the Lunit results. The DLA result was provided as a color map with an abnormality score (%) for thoracic lesions when the score was greater than the predefined cutoff value of 15%. Concordance was achieved when (a) the radiology reports were consistent with the DLA results (“accept”), (b) the radiology reports were partially consistent with the DLA results (“edit”) or had additional lesions compared with the DLA results (“add”). There was discordance when the DLA results were rejected in the radiology report. In addition, we compared the reading times before and after Lunit was introduced. Finally, we evaluated systemic usability scale questionnaire for radiologists and physicians who had experienced Lunit. Results Among 3,113 participants (1,157 men; mean age, 49 years), thoracic abnormalities were found in 343 (11.0%) based on the CXR radiology reports and 621 (20.1%) based on the Lunit results. The concordance rate was 86.8% (accept: 85.3%, edit: 0.9%, and add: 0.6%), and the discordance rate was 13.2%. Except for 479 cases (7.5%) for whom reading time data were unavailable (n = 5) or unreliable (n = 474), the median reading time increased after the clinical integration of Lunit (median, 19s vs. 14s, P < 0.001). Conclusion The real-world multicenter health screening cohort showed a high concordance of the chest X-ray report and the Lunit result under the clinical integration of the deep-learning solution. The reading time slight increased with the Lunit assistance.
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Affiliation(s)
- Eun Young Kim
- Department of Radiology, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea
| | - Young Jae Kim
- Department of Biomedical Engineering, Gachon University College of Medicine, Incheon, South Korea
| | - Won-Jun Choi
- Department of Occupational and Environmental Medicine, Gachon University College of Medicine, Incheon, South Korea
| | - Ji Soo Jeon
- Department of Biomedical Engineering, Gachon University College of Medicine, Incheon, South Korea
| | - Moon Young Kim
- Department of Radiology, Boramae Medical Center, Seoul, South Korea
- Seoul National University College of Medicine, Seoul, South Korea
| | - Dong Hyun Oh
- Department of Radiology, Konyang University Hospital, Daejeon, Korea
- Konyang University School of Medicine, Daejeon, Korea
| | - Kwang Nam Jin
- Department of Radiology, Boramae Medical Center, Seoul, South Korea
- Seoul National University College of Medicine, Seoul, South Korea
- * E-mail: (KNJ); (YJC)
| | - Young Jun Cho
- Department of Radiology, Konyang University Hospital, Daejeon, Korea
- Konyang University School of Medicine, Daejeon, Korea
- * E-mail: (KNJ); (YJC)
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26
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Bove R, Schleimer E, Sukhanov P, Gilson M, Law SM, Barnecut A, Miller BL, Hauser SL, Sanders SJ, Rankin KP. Building a Precision Medicine Delivery Platform for Clinics: The University of California, San Francisco, BRIDGE Experience. J Med Internet Res 2022; 24:e34560. [PMID: 35166689 PMCID: PMC8889486 DOI: 10.2196/34560] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/17/2021] [Accepted: 12/22/2021] [Indexed: 11/24/2022] Open
Abstract
Despite an ever-expanding number of analytics with the potential to impact clinical care, the field currently lacks point-of-care technological tools that allow clinicians to efficiently select disease-relevant data about their patients, algorithmically derive clinical indices (eg, risk scores), and view these data in straightforward graphical formats to inform real-time clinical decisions. Thus far, solutions to this problem have relied on either bottom-up approaches that are limited to a single clinic or generic top-down approaches that do not address clinical users’ specific setting-relevant or disease-relevant needs. As a road map for developing similar platforms, we describe our experience with building a custom but institution-wide platform that enables economies of time, cost, and expertise. The BRIDGE platform was designed to be modular and scalable and was customized to data types relevant to given clinical contexts within a major university medical center. The development process occurred by using a series of human-centered design phases with extensive, consistent stakeholder input. This institution-wide approach yielded a unified, carefully regulated, cross-specialty clinical research platform that can be launched during a patient’s electronic health record encounter. The platform pulls clinical data from the electronic health record (Epic; Epic Systems) as well as other clinical and research sources in real time; analyzes the combined data to derive clinical indices; and displays them in simple, clinician-designed visual formats specific to each disorder and clinic. By integrating an application into the clinical workflow and allowing clinicians to access data sources that would otherwise be cumbersome to assemble, view, and manipulate, institution-wide platforms represent an alternative approach to achieving the vision of true personalized medicine.
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Affiliation(s)
- Riley Bove
- UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Erica Schleimer
- UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Paul Sukhanov
- UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Michael Gilson
- UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Sindy M Law
- UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Andrew Barnecut
- UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Bruce L Miller
- UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Stephen L Hauser
- UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Stephan J Sanders
- UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Katherine P Rankin
- UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
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27
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Craddock M, Crockett C, McWilliam A, Price G, Sperrin M, van der Veer SN, Faivre-Finn C. Evaluation of Prognostic and Predictive Models in the Oncology Clinic. Clin Oncol (R Coll Radiol) 2022; 34:102-113. [PMID: 34922799 DOI: 10.1016/j.clon.2021.11.022] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 11/19/2021] [Accepted: 11/25/2021] [Indexed: 12/13/2022]
Abstract
Predictive and prognostic models hold great potential to support clinical decision making in oncology and could ultimately facilitate a paradigm shift to a more personalised form of treatment. While a large number of models relevant to the field of oncology have been developed, few have been translated into clinical use and assessment of clinical utility is not currently considered a routine part of model development. In this narrative review of the clinical evaluation of prediction models in oncology, we propose a high-level process diagram for the life cycle of a clinical model, encompassing model commissioning, clinical implementation and ongoing quality assurance, which aims to bridge the gap between model development and clinical implementation.
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Affiliation(s)
- M Craddock
- University of Manchester, Radiotherapy Related Research Group, Division of Cancer Sciences, School of Medical Sciences, Manchester, UK.
| | - C Crockett
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
| | - A McWilliam
- University of Manchester, Radiotherapy Related Research Group, Division of Cancer Sciences, School of Medical Sciences, Manchester, UK
| | - G Price
- University of Manchester, Radiotherapy Related Research Group, Division of Cancer Sciences, School of Medical Sciences, Manchester, UK
| | - M Sperrin
- Centre for Health Informatics, Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - S N van der Veer
- Centre for Health Informatics, Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - C Faivre-Finn
- University of Manchester, Radiotherapy Related Research Group, Division of Cancer Sciences, School of Medical Sciences, Manchester, UK; Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
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28
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Bachtiger P, Petri CF, Scott FE, Ri Park S, Kelshiker MA, Sahemey HK, Dumea B, Alquero R, Padam PS, Hatrick IR, Ali A, Ribeiro M, Cheung WS, Bual N, Rana B, Shun-Shin M, Kramer DB, Fragoyannis A, Keene D, Plymen CM, Peters NS. Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study. Lancet Digit Health 2022; 4:e117-e125. [PMID: 34998740 PMCID: PMC8789562 DOI: 10.1016/s2589-7500(21)00256-9] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 10/21/2021] [Accepted: 11/01/2021] [Indexed: 02/06/2023]
Abstract
Background Most patients who have heart failure with a reduced ejection fraction, when left ventricular ejection fraction (LVEF) is 40% or lower, are diagnosed in hospital. This is despite previous presentations to primary care with symptoms. We aimed to test an artificial intelligence (AI) algorithm applied to a single-lead ECG, recorded during ECG-enabled stethoscope examination, to validate a potential point-of-care screening tool for LVEF of 40% or lower. Methods We conducted an observational, prospective, multicentre study of a convolutional neural network (known as AI-ECG) that was previously validated for the detection of reduced LVEF using 12-lead ECG as input. We used AI-ECG retrained to interpret single-lead ECG input alone. Patients (aged ≥18 years) attending for transthoracic echocardiogram in London (UK) were recruited. All participants had 15 s of supine, single-lead ECG recorded at the four standard anatomical positions for cardiac auscultation, plus one handheld position, using an ECG-enabled stethoscope. Transthoracic echocardiogram-derived percentage LVEF was used as ground truth. The primary outcome was performance of AI-ECG at classifying reduced LVEF (LVEF ≤40%), measured using metrics including the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with two-sided 95% CIs. The primary outcome was reported for each position individually and with an optimal combination of AI-ECG outputs (interval range 0–1) from two positions using a rule-based approach and several classification models. This study is registered with ClinicalTrials.gov, NCT04601415. Findings Between Feb 6 and May 27, 2021, we recruited 1050 patients (mean age 62 years [SD 17·4], 535 [51%] male, 432 [41%] non-White). 945 (90%) had an ejection fraction of at least 40%, and 105 (10%) had an ejection fraction of 40% or lower. Across all positions, ECGs were most frequently of adequate quality for AI-ECG interpretation at the pulmonary position (979 [93·3%] of 1050). Quality was lowest for the aortic position (846 [80·6%]). AI-ECG performed best at the pulmonary valve position (p=0·02), with an AUROC of 0·85 (95% CI 0·81–0·89), sensitivity of 84·8% (76·2–91·3), and specificity of 69·5% (66·4–72·6). Diagnostic odds ratios did not differ by age, sex, or non-White ethnicity. Taking the optimal combination of two positions (pulmonary and handheld positions), the rule-based approach resulted in an AUROC of 0·85 (0·81–0·89), sensitivity of 82·7% (72·7–90·2), and specificity of 79·9% (77·0–82·6). Using AI-ECG outputs from these two positions, a weighted logistic regression with l2 regularisation resulted in an AUROC of 0·91 (0·88–0·95), sensitivity of 91·9% (78·1–98·3), and specificity of 80·2% (75·5–84·3). Interpretation A deep learning system applied to single-lead ECGs acquired during a routine examination with an ECG-enabled stethoscope can detect LVEF of 40% or lower. These findings highlight the potential for inexpensive, non-invasive, workflow-adapted, point-of-care screening, for earlier diagnosis and prognostically beneficial treatment. Funding NHS Accelerated Access Collaborative, NHSX, and the National Institute for Health Research.
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29
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McGenity C, Wright A, Treanor D. AIM in Surgical Pathology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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30
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Adegboro CO, Choudhury A, Asan O, Kelly MM. Artificial Intelligence to Improve Health Outcomes in the NICU and PICU: A Systematic Review. Hosp Pediatr 2022; 12:93-110. [PMID: 34890453 DOI: 10.1542/hpeds.2021-006094] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
CONTEXT Artificial intelligence (AI) technologies are increasingly used in pediatrics and have the potential to help inpatient physicians provide high-quality care for critically ill children. OBJECTIVE We aimed to describe the use of AI to improve any health outcome(s) in neonatal and pediatric intensive care. DATA SOURCE PubMed, IEEE Xplore, Cochrane, and Web of Science databases. STUDY SELECTION We used peer-reviewed studies published between June 1, 2010, and May 31, 2020, in which researchers described (1) AI, (2) pediatrics, and (3) intensive care. Studies were included if researchers assessed AI use to improve at least 1 health outcome (eg, mortality). DATA EXTRACTION Data extraction was conducted independently by 2 researchers. Articles were categorized by direct or indirect impact of AI, defined by the European Institute of Innovation and Technology Health joint report. RESULTS Of the 287 publications screened, 32 met inclusion criteria. Approximately 22% (n = 7) of studies revealed a direct impact and improvement in health outcomes after AI implementation. Majority were in prototype testing, and few were deployed into an ICU setting. Among the remaining 78% (n = 25) AI models outperformed standard clinical modalities and may have indirectly influenced patient outcomes. Quantitative assessment of health outcomes using statistical measures, such as area under the receiver operating curve (56%; n = 18) and specificity (38%; n = 12), revealed marked heterogeneity in metrics and standardization. CONCLUSIONS Few studies have revealed that AI has directly improved health outcomes for pediatric critical care patients. Further prospective, experimental studies are needed to assess AI's impact by using established implementation frameworks, standardized metrics, and validated outcome measures.
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Affiliation(s)
- Claudette O Adegboro
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Avishek Choudhury
- Division of Engineering Management, School of Systems and Enterprise, Stevens Institute of Technology, Hoboken, New Jersey
| | - Onur Asan
- Division of Engineering Management, School of Systems and Enterprise, Stevens Institute of Technology, Hoboken, New Jersey
| | - Michelle M Kelly
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
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Mijderwijk HJ, Steiger HJ. Predictive Analytics in Clinical Practice: Advantages and Disadvantages. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:263-268. [PMID: 34862550 DOI: 10.1007/978-3-030-85292-4_30] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Predictive analytics are increasingly reported by clinicians. These tools aim to improve patient outcomes in terms of quality, safety, and efficiency. However, deploying predictive analytics in clinical practice remains challenging today. We highlight several advantages and disadvantages of the application of predictive analytics in clinical practice. To flourish and reach its potential, predictive analytics need data that is of adequate quantity and quality, ideally tailored to clinical scenarios in equipoise regarding optimal management. Adequate reporting of predictive analytic tools is incumbent for uptake into clinical workflows. At least for now, the clinicians' knowledge, experience, and vigilance remain imperative for applying predictive analytics in clinical practice.
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Affiliation(s)
- Hendrik-Jan Mijderwijk
- Department of Neurosurgery, Heinrich Heine University, Medical Faculty, Düsseldorf, Germany.
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Bélisle-Pipon JC, Couture V, Roy MC, Ganache I, Goetghebeur M, Cohen IG. What Makes Artificial Intelligence Exceptional in Health Technology Assessment? Front Artif Intell 2021; 4:736697. [PMID: 34796318 PMCID: PMC8594317 DOI: 10.3389/frai.2021.736697] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 09/23/2021] [Indexed: 12/20/2022] Open
Abstract
The application of artificial intelligence (AI) may revolutionize the healthcare system, leading to enhance efficiency by automatizing routine tasks and decreasing health-related costs, broadening access to healthcare delivery, targeting more precisely patient needs, and assisting clinicians in their decision-making. For these benefits to materialize, governments and health authorities must regulate AI, and conduct appropriate health technology assessment (HTA). Many authors have highlighted that AI health technologies (AIHT) challenge traditional evaluation and regulatory processes. To inform and support HTA organizations and regulators in adapting their processes to AIHTs, we conducted a systematic review of the literature on the challenges posed by AIHTs in HTA and health regulation. Our research question was: What makes artificial intelligence exceptional in HTA? The current body of literature appears to portray AIHTs as being exceptional to HTA. This exceptionalism is expressed along 5 dimensions: 1) AIHT's distinctive features; 2) their systemic impacts on health care and the health sector; 3) the increased expectations towards AI in health; 4) the new ethical, social and legal challenges that arise from deploying AI in the health sector; and 5) the new evaluative constraints that AI poses to HTA. Thus, AIHTs are perceived as exceptional because of their technological characteristics and potential impacts on society at large. As AI implementation by governments and health organizations carries risks of generating new, and amplifying existing, challenges, there are strong arguments for taking into consideration the exceptional aspects of AIHTs, especially as their impacts on the healthcare system will be far greater than that of drugs and medical devices. As AIHTs begin to be increasingly introduced into the health care sector, there is a window of opportunity for HTA agencies and scholars to consider AIHTs' exceptionalism and to work towards only deploying clinically, economically, socially acceptable AIHTs in the health care system.
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Affiliation(s)
| | | | | | - Isabelle Ganache
- Institut National D’Excellence en Santé et en Services Sociaux (INESSS), Montréal, Québec, QC, Canada
| | - Mireille Goetghebeur
- Institut National D’Excellence en Santé et en Services Sociaux (INESSS), Montréal, Québec, QC, Canada
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Herman DS, Rhoads DD, Schulz WL, Durant TJS. Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review. Clin Chem 2021; 67:1466-1482. [PMID: 34557917 DOI: 10.1093/clinchem/hvab165] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 07/26/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND Modern artificial intelligence (AI) and machine learning (ML) methods are now capable of completing tasks with performance characteristics that are comparable to those of expert human operators. As a result, many areas throughout healthcare are incorporating these technologies, including in vitro diagnostics and, more broadly, laboratory medicine. However, there are limited literature reviews of the landscape, likely future, and challenges of the application of AI/ML in laboratory medicine. CONTENT In this review, we begin with a brief introduction to AI and its subfield of ML. The ensuing sections describe ML systems that are currently in clinical laboratory practice or are being proposed for such use in recent literature, ML systems that use laboratory data outside the clinical laboratory, challenges to the adoption of ML, and future opportunities for ML in laboratory medicine. SUMMARY AI and ML have and will continue to influence the practice and scope of laboratory medicine dramatically. This has been made possible by advancements in modern computing and the widespread digitization of health information. These technologies are being rapidly developed and described, but in comparison, their implementation thus far has been modest. To spur the implementation of reliable and sophisticated ML-based technologies, we need to establish best practices further and improve our information system and communication infrastructure. The participation of the clinical laboratory community is essential to ensure that laboratory data are sufficiently available and incorporated conscientiously into robust, safe, and clinically effective ML-supported clinical diagnostics.
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Affiliation(s)
- Daniel S Herman
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel D Rhoads
- Department of Laboratory Medicine, Cleveland Clinic, Cleveland, OH, USA.,Department of Pathology, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Wade L Schulz
- Department of Laboratory Medicine, Yale University, New Haven, CT, USA
| | - Thomas J S Durant
- Department of Laboratory Medicine, Yale University, New Haven, CT, USA
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MALIHA GEORGE, GERKE SARA, COHEN IGLENN, PARIKH RAVIB. Artificial Intelligence and Liability in Medicine: Balancing Safety and Innovation. Milbank Q 2021; 99:629-647. [PMID: 33822422 PMCID: PMC8452365 DOI: 10.1111/1468-0009.12504] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Policy Points With increasing integration of artificial intelligence and machine learning in medicine, there are concerns that algorithm inaccuracy could lead to patient injury and medical liability. While prior work has focused on medical malpractice, the artificial intelligence ecosystem consists of multiple stakeholders beyond clinicians. Current liability frameworks are inadequate to encourage both safe clinical implementation and disruptive innovation of artificial intelligence. Several policy options could ensure a more balanced liability system, including altering the standard of care, insurance, indemnification, special/no-fault adjudication systems, and regulation. Such liability frameworks could facilitate safe and expedient implementation of artificial intelligence and machine learning in clinical care.
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Affiliation(s)
- GEORGE MALIHA
- Perelman School of MedicineUniversity of Pennsylvania
- Department of Internal MedicineUniversity of Pennsylvania
| | - SARA GERKE
- Petrie‐Flom Center for Health Law PolicyBiotechnology, and Bioethics, Harvard Law School, Harvard University
| | - I. GLENN COHEN
- Petrie‐Flom Center for Health Law PolicyBiotechnology, and Bioethics, Harvard Law School, Harvard University
- Harvard Law SchoolHarvard University
| | - RAVI B. PARIKH
- Perelman School of MedicineUniversity of Pennsylvania
- Department of Internal MedicineUniversity of Pennsylvania
- Penn Center for Cancer Care InnovationUniversity of Pennsylvania
- Corporal Michael J. Crescenz VA Medical Center
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Zhang Y, Yang D, Liu Z, Chen C, Ge M, Li X, Luo T, Wu Z, Shi C, Wang B, Huang X, Zhang X, Zhou S, Hei Z. An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation. J Transl Med 2021; 19:321. [PMID: 34321016 PMCID: PMC8317304 DOI: 10.1186/s12967-021-02990-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 07/14/2021] [Indexed: 02/06/2023] Open
Abstract
Background Early prediction of acute kidney injury (AKI) after liver transplantation (LT) facilitates timely recognition and intervention. We aimed to build a risk predictor of post-LT AKI via supervised machine learning and visualize the mechanism driving within to assist clinical decision-making. Methods Data of 894 cases that underwent liver transplantation from January 2015 to September 2019 were collected, covering demographics, donor characteristics, etiology, peri-operative laboratory results, co-morbidities and medications. The primary outcome was new-onset AKI after LT according to Kidney Disease Improving Global Outcomes guidelines. Predicting performance of five classifiers including logistic regression, support vector machine, random forest, gradient boosting machine (GBM) and adaptive boosting were respectively evaluated by the area under the receiver-operating characteristic curve (AUC), accuracy, F1-score, sensitivity and specificity. Model with the best performance was validated in an independent dataset involving 195 adult LT cases from October 2019 to March 2021. SHapley Additive exPlanations (SHAP) method was applied to evaluate feature importance and explain the predictions made by ML algorithms. Results 430 AKI cases (55.1%) were diagnosed out of 780 included cases. The GBM model achieved the highest AUC (0.76, CI 0.70 to 0.82), F1-score (0.73, CI 0.66 to 0.79) and sensitivity (0.74, CI 0.66 to 0.8) in the internal validation set, and a comparable AUC (0.75, CI 0.67 to 0.81) in the external validation set. High preoperative indirect bilirubin, low intraoperative urine output, long anesthesia time, low preoperative platelets, and graft steatosis graded NASH CRN 1 and above were revealed by SHAP method the top 5 important variables contributing to the diagnosis of post-LT AKI made by GBM model. Conclusions Our GBM-based predictor of post-LT AKI provides a highly interoperable tool across institutions to assist decision-making after LT. Graphic abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-02990-4.
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Affiliation(s)
- Yihan Zhang
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, Guangdong, China
| | - Dong Yang
- Guangzhou AID Cloud Technology Co., LTD, Guangzhou, Guangdong, China
| | - Zifeng Liu
- Department of Clinical Data Center, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Chaojin Chen
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, Guangdong, China
| | - Mian Ge
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, Guangdong, China
| | - Xiang Li
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, Guangdong, China
| | - Tongsen Luo
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, Guangdong, China
| | - Zhengdong Wu
- Guangzhou AID Cloud Technology Co., LTD, Guangzhou, Guangdong, China
| | - Chenguang Shi
- Guangzhou AID Cloud Technology Co., LTD, Guangzhou, Guangdong, China
| | - Bohan Wang
- Guangzhou AID Cloud Technology Co., LTD, Guangzhou, Guangdong, China
| | - Xiaoshuai Huang
- Guangzhou AID Cloud Technology Co., LTD, Guangzhou, Guangdong, China
| | - Xiaodong Zhang
- Department of Information, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Shaoli Zhou
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, Guangdong, China
| | - Ziqing Hei
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, Guangdong, China. .,Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, Yuedong Hospital, Meizhou, Guangdong, China.
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Elgazzar FM, Afifi AM, Shama MAE, Askary AE, El-Sarnagawy GN. Development of a risk prediction nomogram for disposition of acute toxic exposure patients to intensive care unit. Basic Clin Pharmacol Toxicol 2021; 129:256-267. [PMID: 34117718 DOI: 10.1111/bcpt.13619] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 05/26/2021] [Indexed: 12/23/2022]
Abstract
Early risk stratification of acutely poisoned patients is essential to identify patients at high risk of intensive care unit (ICU) admission. We aimed to develop a prognostic model and risk-stratification nomogram based on the readily accessible clinical and laboratory predictors on admission for the probability of ICU admission in acutely poisoned patients. This retrospective cohort study included adult patients with acute toxic exposure to a drug or a chemical substance. Patients' demographic, toxicologic, clinical and laboratory data were collected. Among the 1260 eligible patients, 180 (14.3%) were admitted to the ICU. We developed a generalized prognostic model for predicting ICU admission in patients with acute poisoning. The predictors included the Glasgow coma scale, oxygen saturation, diastolic blood pressure, respiratory rate and blood bicarbonate concentration. The model displayed excellent discrimination and calibration (optimistic-adjusted area under the curve = 0.924 and optimistic-adjusted Hosmer and Lemeshow test = 0.922, respectively) when internally validated. Additionally, we developed prognostic models that determine ICU admission in patients with specific poisonings. Furthermore, we constructed risk-stratification nomograms that rank the probability of ICU admission in these patients. The developed risk-stratification nomograms help decision-making regarding ICU admission in acute poisonings. Future external validation in independent cohorts is necessary before clinical application.
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Affiliation(s)
- Fatma M Elgazzar
- Forensic Medicine and Clinical Toxicology Department, Faculty of Medicine, Tanta University, Tanta, Egypt
| | - Ahmed M Afifi
- College of Medicine, University of Kentucky, Lexington, Kentucky, USA
| | - Mohamed Abd Elhady Shama
- Emergency Medicine and Traumatology Department, Faculty of Medicine, Tanta University, Tanta, Egypt
| | - Ahmad El Askary
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
| | - Ghada N El-Sarnagawy
- Forensic Medicine and Clinical Toxicology Department, Faculty of Medicine, Tanta University, Tanta, Egypt
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Abstract
The use of machine learning (ML) in healthcare raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. Specifically, we frame ethics of ML in healthcare through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations. We close by summarizing recommendations to address these challenges.
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Affiliation(s)
- Irene Y Chen
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Emma Pierson
- Microsoft Research, Cambridge, Massachusetts 02143, USA
| | - Sherri Rose
- Center for Health Policy and Center for Primary Care and Outcomes Research, Stanford University, Stanford, California 94305, USA
| | | | - Kadija Ferryman
- Department of Technology, Culture, and Society, Tandon School of Engineering, New York University, Brooklyn, New York 11201, USA
| | - Marzyeh Ghassemi
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Institute for Medical and Evaluative Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
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Mirbabaie M, Hofeditz L, Frick NRJ, Stieglitz S. Artificial intelligence in hospitals: providing a status quo of ethical considerations in academia to guide future research. AI & SOCIETY 2021; 37:1361-1382. [PMID: 34219989 PMCID: PMC8238382 DOI: 10.1007/s00146-021-01239-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 05/25/2021] [Indexed: 12/23/2022]
Abstract
The application of artificial intelligence (AI) in hospitals yields many advantages but also confronts healthcare with ethical questions and challenges. While various disciplines have conducted specific research on the ethical considerations of AI in hospitals, the literature still requires a holistic overview. By conducting a systematic discourse approach highlighted by expert interviews with healthcare specialists, we identified the status quo of interdisciplinary research in academia on ethical considerations and dimensions of AI in hospitals. We found 15 fundamental manuscripts by constructing a citation network for the ethical discourse, and we extracted actionable principles and their relationships. We provide an agenda to guide academia, framed under the principles of biomedical ethics. We provide an understanding of the current ethical discourse of AI in clinical environments, identify where further research is pressingly needed, and discuss additional research questions that should be addressed. We also guide practitioners to acknowledge AI-related benefits in hospitals and to understand the related ethical concerns. Supplementary Information The online version contains supplementary material available at 10.1007/s00146-021-01239-4.
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Affiliation(s)
- Milad Mirbabaie
- Faculty of Business Administration and Economics, Paderborn University, Paderborn, Germany
| | - Lennart Hofeditz
- Professional Communication in Electronic Media / Social Media, University of Duisburg-Essen, Duisburg, Germany
| | - Nicholas R. J. Frick
- Professional Communication in Electronic Media / Social Media, University of Duisburg-Essen, Duisburg, Germany
| | - Stefan Stieglitz
- Professional Communication in Electronic Media / Social Media, University of Duisburg-Essen, Duisburg, Germany
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Pérez-Rodríguez D, López-Fernández H, Agís-Balboa RC. Application of miRNA-seq in neuropsychiatry: A methodological perspective. Comput Biol Med 2021; 135:104603. [PMID: 34216893 DOI: 10.1016/j.compbiomed.2021.104603] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/21/2021] [Accepted: 06/21/2021] [Indexed: 10/21/2022]
Abstract
MiRNAs are emerging as key molecules to study neuropsychiatric diseases. However, despite the large number of methodologies and software for miRNA-seq analyses, there is little supporting literature for researchers in this area. This review focuses on evaluating how miRNA-seq has been used to study neuropsychiatric diseases to date, analyzing both the main findings discovered and the bioinformatics workflows and tools used from a methodological perspective. The objective of this review is two-fold: first, to evaluate current miRNA-seq procedures used in neuropsychiatry; and second, to offer comprehensive information that can serve as a guide to new researchers in bioinformatics. After conducting a systematic search (from 2016 to June 30, 2020) of articles using miRNA-seq in neuropsychiatry, we have seen that it has already been used for different types of studies in three main categories: diagnosis, prognosis, and mechanism. We carefully analyzed the bioinformatics workflows of each study, observing a high degree of variability with respect to the tools and methods used and several methodological complexities that are identified and discussed in this review.
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Affiliation(s)
- Daniel Pérez-Rodríguez
- Translational Neuroscience Group-CIBERSAM, Galicia Sur Health Research Institute (IIS Galicia Sur), Área Sanitaria de Vigo-Hospital Álvaro Cunqueiro, SERGAS-UVIGO, 36213, Vigo, Spain; NeuroEpigenetics Lab. University Hospital Complex of Vigo, SERGAS-UVIGO, 36213, Vigo, Spain
| | - Hugo López-Fernández
- Instituto de Investigação e Inovação Em Saúde (I3S), Universidade Do Porto, Rua Alfredo Allen, 208, 4200-135, Porto, Portugal; CINBIO, Universidade de Vigo, Department of Computer Science, ESEI - Escuela Superior de Ingeniería Informática, 32004, Ourense, Spain; SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain.
| | - Roberto C Agís-Balboa
- Translational Neuroscience Group-CIBERSAM, Galicia Sur Health Research Institute (IIS Galicia Sur), Área Sanitaria de Vigo-Hospital Álvaro Cunqueiro, SERGAS-UVIGO, 36213, Vigo, Spain; NeuroEpigenetics Lab. University Hospital Complex of Vigo, SERGAS-UVIGO, 36213, Vigo, Spain.
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Pezzo MV, Nash BED, Vieux P, Foster-Grammer HW. Effect of Having, but Not Consulting, a Computerized Diagnostic Aid. Med Decis Making 2021; 42:94-104. [PMID: 33966519 DOI: 10.1177/0272989x211011160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Previous research has described physicians' reluctance to use computerized diagnostic aids (CDAs) but has never experimentally examined the effects of not consulting an aid that was readily available. Experiment 1. Participants read about a diagnosis made either by a physician or an auto mechanic (to control for perceived expertise). Half read that a CDA was available but never actually consulted; no mention of a CDA was made for the remaining half. For the physician, failure to consult the CDA had no significant effect on competence ratings for either the positive or negative outcome. For the auto mechanic, failure to consult the CDA actually increased competence ratings following a negative but not a positive outcome. Negligence judgments were greater for the mechanic than for the physician overall. Experiment 2. Using only a negative outcome, we included 2 different reasons for not consulting the aid and provided accuracy information highlighting the superiority of the CDA over the physician. In neither condition was the physician rated lower than when no aid was mentioned. Ratings were lower when the physician did not trust the CDA and, surprisingly, higher when the physician believed he or she already knew what the CDA would say. Finally, consistent with our previous research, ratings were also high when the physician consulted and then followed the advice of a CDA and low when the CDA was consulted but ignored. Individual differences in numeracy did not qualify these results. Implications for the literature on algorithm aversion and clinical practice are discussed.
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Affiliation(s)
- Mark V Pezzo
- University of South Florida, St. Petersburg, FL, USA
| | | | - Pierre Vieux
- University of South Florida, St. Petersburg, FL, USA
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Lundon DJ, Kelly BD, Nair S, Bolton DM, Patel G, Reich D, Tewari A. A COVID-19 Test Triage Tool, Predicting Negative Results and Reducing the Testing Burden on Healthcare Systems During a Pandemic. Front Med (Lausanne) 2021; 8:563465. [PMID: 33996839 PMCID: PMC8116585 DOI: 10.3389/fmed.2021.563465] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 03/19/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Detecting and isolating cases of COVID-19 are amongst the key elements listed by the WHO to reduce transmission. This approach has been reported to reduce those symptomatic with COVID-19 in the population by over 90%. Testing is part of a strategy that will save lives. Testing everyone maybe ideal, but it is not practical. A risk tool based on patient demographics and clinical parameters has the potential to help identify patients most likely to test negative for SARS-CoV-2. If effective it could be used to aide clinical decision making and reduce the testing burden. Methods: At the time of this analysis, a total of 9,516 patients with symptoms suggestive of Covid-19, were assessed and tested at Mount Sinai Institutions in New York. Patient demographics, clinical parameters and test results were collected. A robust prediction pipeline was used to develop a risk tool to predict the likelihood of a positive test for Covid-19. The risk tool was analyzed in a holdout dataset from the cohort and its discriminative ability, calibration and net benefit assessed. Results: Over 48% of those tested in this cohort, had a positive result. The derived model had an AUC of 0.77, provided reliable risk prediction, and demonstrated a superior net benefit than a strategy of testing everybody. When a risk cut-off of 70% was applied, the model had a negative predictive value of 96%. Conclusion: Such a tool could be used to help aide but not replace clinical decision making and conserve vital resources needed to effectively tackle this pandemic.
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Affiliation(s)
- Dara J Lundon
- Department of Urology, Icahn School of Medicine, Mount Sinai Hospitals, New York, NY, United States
| | - Brian D Kelly
- Department of Urology, Austin Health, Melbourne, VIC, Australia
| | - Sujit Nair
- Department of Urology, Icahn School of Medicine, Mount Sinai Hospitals, New York, NY, United States
| | - Damien M Bolton
- Department of Urology, Austin Health, Melbourne, VIC, Australia
| | - Gopi Patel
- Department of Infectious Diseases, Icahn School of Medicine, Mount Sinai Hospitals, New York, NY, United States
| | - David Reich
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Ashutosh Tewari
- Department of Urology, Icahn School of Medicine, Mount Sinai Hospitals, New York, NY, United States
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Integrated Analytical Framework for the Development of Artificial Intelligence-Based Medical Devices. Ther Innov Regul Sci 2021; 55:853-865. [PMID: 33876397 DOI: 10.1007/s43441-021-00292-x] [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: 11/22/2020] [Accepted: 04/02/2021] [Indexed: 10/21/2022]
Abstract
INTRODUCTION A persistent fundamental problem in applying artificial intelligence (AI) to medical devices is achieving high performance while guaranteeing the safety and reliability of AI solutions. However, the regulation of medical devices and that of AI implementations both differ among countries, resulting in "double-layered" differences in the regulations for medical devices employing AI. METHODS To overcome these differences and examine the universal requirements for AI-based medical devices, we clarify legal and ethical issues, regulatory requirements, and terms in representative guidances related to AI-based medical devices from different countries. Then, we propose an integrated framework for analyzing the requirements of AI-based medical devices for a comprehensive comparison. RESULTS We demonstrate that no guidance completely covers all the evaluation requirements, and the differences in coverage per country may direct the international harmonization of these requirements. DISCUSSION Consequently, we show two development strategies available to manufacturers, namely adherence to local regulations with tedious efforts or compliance with universal requirements using our integrated framework even under the low level of international harmonization; the latter is recommended for efficiency and convergence.
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Park SH, Choi J, Byeon JS. Key Principles of Clinical Validation, Device Approval, and Insurance Coverage Decisions of Artificial Intelligence. Korean J Radiol 2021; 22:442-453. [PMID: 33629545 PMCID: PMC7909857 DOI: 10.3348/kjr.2021.0048] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 01/15/2021] [Accepted: 01/15/2021] [Indexed: 01/17/2023] Open
Abstract
Artificial intelligence (AI) will likely affect various fields of medicine. This article aims to explain the fundamental principles of clinical validation, device approval, and insurance coverage decisions of AI algorithms for medical diagnosis and prediction. Discrimination accuracy of AI algorithms is often evaluated with the Dice similarity coefficient, sensitivity, specificity, and traditional or free-response receiver operating characteristic curves. Calibration accuracy should also be assessed, especially for algorithms that provide probabilities to users. As current AI algorithms have limited generalizability to real-world practice, clinical validation of AI should put it to proper external testing and assisting roles. External testing could adopt diagnostic case-control or diagnostic cohort designs. A diagnostic case-control study evaluates the technical validity/accuracy of AI while the latter tests the clinical validity/accuracy of AI in samples representing target patients in real-world clinical scenarios. Ultimate clinical validation of AI requires evaluations of its impact on patient outcomes, referred to as clinical utility, and for which randomized clinical trials are ideal. Device approval of AI is typically granted with proof of technical validity/accuracy and thus does not intend to directly indicate if AI is beneficial for patient care or if it improves patient outcomes. Neither can it categorically address the issue of limited generalizability of AI. After achieving device approval, it is up to medical professionals to determine if the approved AI algorithms are beneficial for real-world patient care. Insurance coverage decisions generally require a demonstration of clinical utility that the use of AI has improved patient outcomes.
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Affiliation(s)
- Seong Ho Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
| | - Jaesoon Choi
- Department of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jeong Sik Byeon
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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Wawira Gichoya J, McCoy LG, Celi LA, Ghassemi M. Equity in essence: a call for operationalising fairness in machine learning for healthcare. BMJ Health Care Inform 2021; 28:e100289. [PMID: 33910923 PMCID: PMC8733939 DOI: 10.1136/bmjhci-2020-100289] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 02/07/2021] [Accepted: 02/09/2021] [Indexed: 12/22/2022] Open
Affiliation(s)
- Judy Wawira Gichoya
- Department of Radiology & Imaging Sciences, Emory University, Atlanta, Georgia, USA
- Fogarty International Center, National Institutes of Health (NIH), Bethesda, Maryland, USA
| | - Liam G McCoy
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Harvard-MIT Division of Health Sciences and Technology, Cambridge, Massachusetts, USA
- Division of Pulmonary Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Biostatistics, Harvrd T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Marzyeh Ghassemi
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
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Zielinski JM, Luke JJ, Guglietta S, Krieg C. High Throughput Multi-Omics Approaches for Clinical Trial Evaluation and Drug Discovery. Front Immunol 2021; 12:590742. [PMID: 33868223 PMCID: PMC8044891 DOI: 10.3389/fimmu.2021.590742] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Accepted: 03/01/2021] [Indexed: 12/12/2022] Open
Abstract
High throughput single cell multi-omics platforms, such as mass cytometry (cytometry by time-of-flight; CyTOF), high dimensional imaging (>6 marker; Hyperion, MIBIscope, CODEX, MACSima) and the recently evolved genomic cytometry (Citeseq or REAPseq) have enabled unprecedented insights into many biological and clinical questions, such as hematopoiesis, transplantation, cancer, and autoimmunity. In synergy with constantly adapting new single-cell analysis approaches and subsequent accumulating big data collections from these platforms, whole atlases of cell types and cellular and sub-cellular interaction networks are created. These atlases build an ideal scientific discovery environment for reference and data mining approaches, which often times reveals new cellular disease networks. In this review we will discuss how combinations and fusions of different -omic workflows on a single cell level can be used to examine cellular phenotypes, immune effector functions, and even dynamic changes, such as metabolomic state of different cells in a sample or even in a defined tissue location. We will touch on how pre-print platforms help in optimization and reproducibility of workflows, as well as community outreach. We will also shortly discuss how leveraging single cell multi-omic approaches can be used to accelerate cellular biomarker discovery during clinical trials to predict response to therapy, follow responsive cell types, and define novel druggable target pathways. Single cell proteome approaches already have changed how we explore cellular mechanism in disease and during therapy. Current challenges in the field are how we share these disruptive technologies to the scientific communities while still including new approaches, such as genomic cytometry and single cell metabolomics.
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Affiliation(s)
- Jessica M. Zielinski
- Hollings Cancer Center, Medical University of South Carolina (MUSC), Charleston, SC, United States
| | - Jason J. Luke
- Hillman Cancer Center, Department of Medicine, Division of Hematology/Oncology, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Silvia Guglietta
- Hollings Cancer Center, Medical University of South Carolina (MUSC), Charleston, SC, United States
| | - Carsten Krieg
- Hollings Cancer Center, Medical University of South Carolina (MUSC), Charleston, SC, United States
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Feurle GE, Moos V, Stroux A, Gehrmann-Sommer N, Poddubnyy D, Fiehn C, Schneider T. Differential diagnostic value of rheumatic symptoms in patients with Whipple's disease. Sci Rep 2021; 11:5980. [PMID: 33727566 PMCID: PMC7966399 DOI: 10.1038/s41598-021-85217-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 02/15/2021] [Indexed: 12/17/2022] Open
Abstract
Most patients with Whipple’s disease have rheumatic symptoms. The aim of our prospective, questionnaire-based, non-interventional clinical study was to assess whether these symptoms are useful in guiding the differential diagnosis to the rheumatic disorders. Forty patients with Whipple’s disease, followed by 20 patients for validation and 30 patients with rheumatoid-, 21 with psoriatic-, 15 with palindromic- and 25 with axial spondyloarthritis were recruited for the present investigation. Patients with Whipple’s disease and patients with rheumatic disorders were asked to record rheumatic symptoms on pseudonymized questionnaires. The data obtained were subjected to multiple logistic regression analysis. Episodic pain with rapid onset, springing from joint to joint was most common in patients with palindromic arthritis and second most common and somewhat less conspicuous in Whipple’s disease. Continuous pain in the same joints predominated in patients with rheumatoid-, psoriatic-, and axial spondyloarthritis. Multiple logistic equations resulted in a predicted probability for the diagnosis of Whipple’s disease of 43.4 ± 0.19% (M ± SD) versus a significantly lower probability of 23.8 ± 0.19% (M ± SD) in the aggregate of patients with rheumatic disorders. Mean area under the curve (AUC) ± SD was 0.781 ± 0.044, 95% CI 0.695–0.867, asymptotic significance p < 0.001. The logistic equations predicted probability for the diagnosis of Whipple’s disease in the initial series of 40 patients of 43.4 ± 0.19% was not significantly different in the subsequent 20 patients of 38.2 ± 0.28% (M ± SD) (p = 0.376). The data may be useful in a predictive algorithm for diagnosing Whipple’s disease. The project is registered as clinical study DRK S0001566.
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Affiliation(s)
- Gerhard E Feurle
- DRK Krankenhaus Neuwied, Eduard Moerikestrasse 12, 56567, Neuwied, Germany.
| | - Verena Moos
- Medizinische Klinik I für Gastroenterologie, Infektiologie und Rheumatologie, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Andrea Stroux
- Institut für Biometrie und Klinische Epidemiologie, Charité-Universitätsmedizin Berlin, Berlin Institute of Health, Berlin, Germany
| | - Nadine Gehrmann-Sommer
- Medizinische Klinik I für Gastroenterologie, Infektiologie und Rheumatologie, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Denis Poddubnyy
- Medizinische Klinik I für Gastroenterologie, Infektiologie und Rheumatologie, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Christoph Fiehn
- Praxis für Rheumatologie, Klinische Immunologie, Medical Center, Baden-Baden, Germany
| | - Thomas Schneider
- Medizinische Klinik I für Gastroenterologie, Infektiologie und Rheumatologie, Charité-Universitätsmedizin Berlin, Berlin, Germany
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Shi C, Zhou Z, Lin H, Gao J. Imaging Beyond Seeing: Early Prognosis of Cancer Treatment. SMALL METHODS 2021; 5:e2001025. [PMID: 34927817 DOI: 10.1002/smtd.202001025] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 11/24/2020] [Indexed: 06/14/2023]
Abstract
Assessing cancer response to therapeutic interventions has been realized as an important course to early predict curative efficacy and treatment outcomes due to tumor heterogeneity. Compared to the traditional invasive tissue biopsy method, molecular imaging techniques have fundamentally revolutionized the ability to evaluate cancer response in a spatiotemporal manner. The past few years has witnessed a paradigm shift on the efforts from manufacturing functional molecular imaging probes for seeing a tumor to a vantage stage of interpreting the tumor response during different treatments. This review is to stand by the current development of advanced imaging technologies aiming to predict the treatment response in cancer therapy. Special interest is placed on the systems that are able to provide rapid and noninvasive assessment of pharmacokinetic drug fates (e.g., drug distribution, release, and activation) and tumor microenvironment heterogeneity (e.g., tumor cells, macrophages, dendritic cells (DCs), T cells, and inflammatory cells). The current status, practical significance, and future challenges of the emerging artificial intelligence (AI) technology and machine learning in the applications of medical imaging fields is overviewed. Ultimately, the authors hope that this review is timely to spur research interest in molecular imaging and precision medicine.
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Affiliation(s)
- Changrong Shi
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics and Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, 361102, China
| | - Zijian Zhou
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics and Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, 361102, China
| | - Hongyu Lin
- State Key Laboratory of Physical Chemistry of Solid Surfaces, The Key Laboratory for Chemical Biology of Fujian Province and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Jinhao Gao
- State Key Laboratory of Physical Chemistry of Solid Surfaces, The Key Laboratory for Chemical Biology of Fujian Province and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
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Gaube S, Suresh H, Raue M, Merritt A, Berkowitz SJ, Lermer E, Coughlin JF, Guttag JV, Colak E, Ghassemi M. Do as AI say: susceptibility in deployment of clinical decision-aids. NPJ Digit Med 2021; 4:31. [PMID: 33608629 PMCID: PMC7896064 DOI: 10.1038/s41746-021-00385-9] [Citation(s) in RCA: 112] [Impact Index Per Article: 37.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 01/07/2021] [Indexed: 02/07/2023] Open
Abstract
Artificial intelligence (AI) models for decision support have been developed for clinical settings such as radiology, but little work evaluates the potential impact of such systems. In this study, physicians received chest X-rays and diagnostic advice, some of which was inaccurate, and were asked to evaluate advice quality and make diagnoses. All advice was generated by human experts, but some was labeled as coming from an AI system. As a group, radiologists rated advice as lower quality when it appeared to come from an AI system; physicians with less task-expertise did not. Diagnostic accuracy was significantly worse when participants received inaccurate advice, regardless of the purported source. This work raises important considerations for how advice, AI and non-AI, should be deployed in clinical environments.
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Affiliation(s)
- Susanne Gaube
- Department of Psychology, University of Regensburg, Regensburg, Germany. .,MIT AgeLab, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Harini Suresh
- MIT Computer Science & Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Martina Raue
- MIT AgeLab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Seth J Berkowitz
- Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Eva Lermer
- LMU Center for Leadership and People Management, LMU Munich, Munich, Germany.,FOM University of Applied Sciences for Economics & Management, Munich, Germany
| | - Joseph F Coughlin
- MIT AgeLab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - John V Guttag
- MIT Computer Science & Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Errol Colak
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada.,Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Marzyeh Ghassemi
- Departments of Computer Science and Medicine, University of Toronto, Toronto, Canada.,Vector Institute, Toronto, Canada
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49
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Jayakumar P, Moore MG, Furlough KA, Uhler LM, Andrawis JP, Koenig KM, Aksan N, Rathouz PJ, Bozic KJ. Comparison of an Artificial Intelligence-Enabled Patient Decision Aid vs Educational Material on Decision Quality, Shared Decision-Making, Patient Experience, and Functional Outcomes in Adults With Knee Osteoarthritis: A Randomized Clinical Trial. JAMA Netw Open 2021; 4:e2037107. [PMID: 33599773 PMCID: PMC7893500 DOI: 10.1001/jamanetworkopen.2020.37107] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
IMPORTANCE Decision aids can help inform appropriate selection of total knee replacement (TKR) for advanced knee osteoarthritis (OA). However, few decision aids combine patient education, preference assessment, and artificial intelligence (AI) using patient-reported outcome measurement data to generate personalized estimations of outcomes to augment shared decision-making (SDM). OBJECTIVE To assess the effect of an AI-enabled patient decision aid that includes education, preference assessment, and personalized outcome estimations (using patient-reported outcome measurements) on decision quality, patient experience, functional outcomes, and process-level outcomes among individuals with advanced knee OA considering TKR in comparison with education only. DESIGN, SETTING, AND PARTICIPANTS This randomized clinical trial at a single US academic orthopedic practice included 129 new adult patients presenting for OA-related knee pain from March 2019 to January 2020. Data were analyzed from April to May 2020. INTERVENTION Patients were randomized into a group that received a decision aid including patient education, preference assessment, and personalized outcome estimations (intervention group) or a group receiving educational material only (control group) alongside usual care. MAIN OUTCOMES AND MEASURES The primary outcome was decision quality, measured using the Knee OA Decision Quality Instrument (K-DQI). Secondary outcomes were collaborative decision-making (assessed using the CollaboRATE survey), patient satisfaction with consultation (using a numerical rating scale), Knee Injury and Osteoarthritis Outcome Score Joint Replacement (KOOS JR) score, consultation time, TKR rate, and treatment concordance. RESULTS A total of 69 patients in the intervention group (46 [67%] women) and 60 patients in the control group (37 [62%] women) were included in the analysis. The intervention group showed better decisional quality (K-DQI mean difference, 20.0%; SE, 3.02; 95% CI, 14.2%-26.1%; P < .001), collaborative decision-making (CollaboRATE, 8 of 69 [12%] vs 28 of 60 [47%] patients below median; P < .001), satisfaction (numerical rating scale, 9 of 65 [14%] vs 19 of 58 [33%] patients below median; P = .01), and improved functional outcomes at 4 to 6 months (mean [SE] KOOS JR, 4.9 [2.24] points higher in intervention group; 95% CI, 0.8-9.0 points; P = .02). The intervention did not significantly affect consultation time (mean [SE] difference, 2.23 [2.18] minutes; P = .31), TKR rates (16 of 69 [23%] vs 7 of 60 [12%] patients; P = .11), or treatment concordance (58 of 69 [84%] vs 44 of 60 [73%] patients; P = .19). CONCLUSIONS AND RELEVANCE In this randomized clinical trial, an AI-enabled decision aid significantly improved decision quality, level of SDM, satisfaction, and physical limitations without significantly impacting consultation times, TKR rates, or treatment concordance in patients with knee OA considering TKR. Decision aids using a personalized, data-driven approach can enhance SDM in the management of knee OA. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT03956004.
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Affiliation(s)
| | - Meredith G. Moore
- Dell Medical School at the University of Texas at Austin, Austin
- University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Kenneth A. Furlough
- Dell Medical School at the University of Texas at Austin, Austin
- Chicago Medical School, North Chicago, Illinois
| | - Lauren M. Uhler
- Dell Medical School at the University of Texas at Austin, Austin
| | - John P. Andrawis
- Dell Medical School at the University of Texas at Austin, Austin
- Harbor-UCLA Medical Center, West Carson, California
| | - Karl M. Koenig
- Dell Medical School at the University of Texas at Austin, Austin
| | - Nazan Aksan
- Dell Medical School at the University of Texas at Austin, Austin
| | - Paul J. Rathouz
- Dell Medical School at the University of Texas at Austin, Austin
| | - Kevin J. Bozic
- Dell Medical School at the University of Texas at Austin, Austin
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50
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Panagiotou OA, Högg LH, Hricak H, Khleif SN, Levy MA, Magnus D, Murphy MJ, Patel B, Winn RA, Nass SJ, Gatsonis C, Cogle CR. Clinical Application of Computational Methods in Precision Oncology: A Review. JAMA Oncol 2021; 6:1282-1286. [PMID: 32407443 DOI: 10.1001/jamaoncol.2020.1247] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Importance There is an enormous and growing amount of data available from individual cancer cases, which makes the work of clinical oncologists more demanding. This data challenge has attracted engineers to create software that aims to improve cancer diagnosis or treatment. However, the move to use computers in the oncology clinic for diagnosis or treatment has led to instances of premature or inappropriate use of computational predictive systems. Objective To evaluate best practices for developing and assessing the clinical utility of predictive computational methods in oncology. Evidence Review The National Cancer Policy Forum and the Board on Mathematical Sciences and Analytics at the National Academies of Sciences, Engineering, and Medicine hosted a workshop to examine the use of multidimensional data derived from patients with cancer and the computational methods used to analyze these data. The workshop convened diverse stakeholders and experts, including computer scientists, oncology clinicians, statisticians, patient advocates, industry leaders, ethicists, leaders of health systems (academic and community based), private and public health insurance carriers, federal agencies, and regulatory authorities. Key characteristics for successful computational oncology were considered in 3 thematic areas: (1) data quality, completeness, sharing, and privacy; (2) computational methods for analysis, interpretation, and use of oncology data; and (3) clinical infrastructure and expertise for best use of computational precision oncology. Findings Quality control was found to be essential across all stages, from data collection to data processing, management, and use. Collecting a standardized parsimonious data set at every cancer diagnosis and restaging could enhance reliability and completeness of clinical data for precision oncology. Data completeness refers to key data elements such as information about cancer diagnosis, treatment, and outcomes, while data quality depends on whether appropriate variables have been measured in valid and reliable ways. Collecting data from diverse populations can reduce the risk of creating invalid and biased algorithms. Computational systems that aid clinicians should be classified as software as a medical device and thus regulated according to the potential risk posed. To facilitate appropriate use of computational methods that interpret high-dimensional data in oncology, treating physicians need access to multidisciplinary teams with broad expertise and deep training among a subset of clinical oncology fellows in clinical informatics. Conclusions and Relevance Workshop discussions suggested best practices in demonstrating the clinical utility of predictive computational methods for diagnosing or treating cancer.
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Affiliation(s)
- Orestis A Panagiotou
- Department of Health Services, Policy and Practice, Brown University School of Public Health, Providence, Rhode Island
| | - Lori Hoffman Högg
- National Center for Health Promotion and Disease Prevention, Veterans Health Administration, Durham, North Carolina.,Office of Nursing Services, Veterans Health Administration, Washington, DC
| | - Hedvig Hricak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Samir N Khleif
- Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC
| | - Mia A Levy
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee.,Division of Hematology and Oncology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - David Magnus
- Center for Biomedical Ethics, Stanford University School of Medicine, Stanford, California
| | | | - Bakul Patel
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland
| | - Robert A Winn
- University of Illinois at Chicago Cancer Center, University of Illinois Hospital and Health Sciences System, Chicago
| | - Sharyl J Nass
- Health and Medicine Division, National Academies of Sciences, Engineering, and Medicine, Washington, DC
| | - Constantine Gatsonis
- Department of Biostatistics, Brown University School of Public Health, Providence, Rhode Island
| | - Christopher R Cogle
- Division of Hematology & Oncology, Department of Medicine, University of Florida College of Medicine, Gainesville
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