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Balagopalan A, Baldini I, Celi LA, Gichoya J, McCoy LG, Naumann T, Shalit U, van der Schaar M, Wagstaff KL. Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact. PLOS DIGITAL HEALTH 2024; 3:e0000474. [PMID: 38620047 PMCID: PMC11018283 DOI: 10.1371/journal.pdig.0000474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 02/18/2024] [Indexed: 04/17/2024]
Abstract
Despite significant technical advances in machine learning (ML) over the past several years, the tangible impact of this technology in healthcare has been limited. This is due not only to the particular complexities of healthcare, but also due to structural issues in the machine learning for healthcare (MLHC) community which broadly reward technical novelty over tangible, equitable impact. We structure our work as a healthcare-focused echo of the 2012 paper "Machine Learning that Matters", which highlighted such structural issues in the ML community at large, and offered a series of clearly defined "Impact Challenges" to which the field should orient itself. Drawing on the expertise of a diverse and international group of authors, we engage in a narrative review and examine issues in the research background environment, training processes, evaluation metrics, and deployment protocols which act to limit the real-world applicability of MLHC. Broadly, we seek to distinguish between machine learning ON healthcare data and machine learning FOR healthcare-the former of which sees healthcare as merely a source of interesting technical challenges, and the latter of which regards ML as a tool in service of meeting tangible clinical needs. We offer specific recommendations for a series of stakeholders in the field, from ML researchers and clinicians, to the institutions in which they work, and the governments which regulate their data access.
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Affiliation(s)
- Aparna Balagopalan
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology; Cambridge, Massachusetts, United States of America
| | - Ioana Baldini
- IBM Research; Yorktown Heights, New York, United States of America
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology; Cambridge, Massachusetts, United States of America
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center; Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health; Boston, Massachusetts, United States of America
| | - Judy Gichoya
- Department of Radiology and Imaging Sciences, School of Medicine, Emory University; Atlanta, Georgia, United States of America
| | - Liam G. McCoy
- Division of Neurology, Department of Medicine, University of Alberta; Edmonton, Alberta, Canada
| | - Tristan Naumann
- Microsoft Research; Redmond, Washington, United States of America
| | - Uri Shalit
- The Faculty of Data and Decision Sciences, Technion; Haifa, Israel
| | - Mihaela van der Schaar
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge; Cambridge, United Kingdom
- The Alan Turing Institute; London, United Kingdom
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Anand A, Krithivasan S, Roy K. RoMIA: a framework for creating Robust Medical Imaging AI models for chest radiographs. FRONTIERS IN RADIOLOGY 2024; 3:1274273. [PMID: 38260820 PMCID: PMC10800823 DOI: 10.3389/fradi.2023.1274273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024]
Abstract
Artificial Intelligence (AI) methods, particularly Deep Neural Networks (DNNs), have shown great promise in a range of medical imaging tasks. However, the susceptibility of DNNs to producing erroneous outputs under the presence of input noise and variations is of great concern and one of the largest challenges to their adoption in medical settings. Towards addressing this challenge, we explore the robustness of DNNs trained for chest radiograph classification under a range of perturbations reflective of clinical settings. We propose RoMIA, a framework for the creation of Robust Medical Imaging AI models. RoMIA adds three key steps to the model training and deployment flow: (i) Noise-added training, wherein a part of the training data is synthetically transformed to represent common noise sources, (ii) Fine-tuning with input mixing, in which the model is refined with inputs formed by mixing data from the original training set with a small number of images from a different source, and (iii) DCT-based denoising, which removes a fraction of high-frequency components of each image before applying the model to classify it. We applied RoMIA to create six different robust models for classifying chest radiographs using the CheXpert dataset. We evaluated the models on the CheXphoto dataset, which consists of naturally and synthetically perturbed images intended to evaluate robustness. Models produced by RoMIA show 3%-5% improvement in robust accuracy, which corresponds to an average reduction of 22.6% in misclassifications. These results suggest that RoMIA can be a useful step towards enabling the adoption of AI models in medical imaging applications.
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Affiliation(s)
- Aditi Anand
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States
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Brereton TA, Malik MM, Lifson M, Greenwood JD, Peterson KJ, Overgaard SM. The Role of Artificial Intelligence Model Documentation in Translational Science: Scoping Review. Interact J Med Res 2023; 12:e45903. [PMID: 37450330 PMCID: PMC10382950 DOI: 10.2196/45903] [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: 01/25/2023] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND Despite the touted potential of artificial intelligence (AI) and machine learning (ML) to revolutionize health care, clinical decision support tools, herein referred to as medical modeling software (MMS), have yet to realize the anticipated benefits. One proposed obstacle is the acknowledged gaps in AI translation. These gaps stem partly from the fragmentation of processes and resources to support MMS transparent documentation. Consequently, the absence of transparent reporting hinders the provision of evidence to support the implementation of MMS in clinical practice, thereby serving as a substantial barrier to the successful translation of software from research settings to clinical practice. OBJECTIVE This study aimed to scope the current landscape of AI- and ML-based MMS documentation practices and elucidate the function of documentation in facilitating the translation of ethical and explainable MMS into clinical workflows. METHODS A scoping review was conducted in accordance with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. PubMed was searched using Medical Subject Headings key concepts of AI, ML, ethical considerations, and explainability to identify publications detailing AI- and ML-based MMS documentation, in addition to snowball sampling of selected reference lists. To include the possibility of implicit documentation practices not explicitly labeled as such, we did not use documentation as a key concept but as an inclusion criterion. A 2-stage screening process (title and abstract screening and full-text review) was conducted by 1 author. A data extraction template was used to record publication-related information; barriers to developing ethical and explainable MMS; available standards, regulations, frameworks, or governance strategies related to documentation; and recommendations for documentation for papers that met the inclusion criteria. RESULTS Of the 115 papers retrieved, 21 (18.3%) papers met the requirements for inclusion. Ethics and explainability were investigated in the context of AI- and ML-based MMS documentation and translation. Data detailing the current state and challenges and recommendations for future studies were synthesized. Notable themes defining the current state and challenges that required thorough review included bias, accountability, governance, and explainability. Recommendations identified in the literature to address present barriers call for a proactive evaluation of MMS, multidisciplinary collaboration, adherence to investigation and validation protocols, transparency and traceability requirements, and guiding standards and frameworks that enhance documentation efforts and support the translation of AI- and ML-based MMS. CONCLUSIONS Resolving barriers to translation is critical for MMS to deliver on expectations, including those barriers identified in this scoping review related to bias, accountability, governance, and explainability. Our findings suggest that transparent strategic documentation, aligning translational science and regulatory science, will support the translation of MMS by coordinating communication and reporting and reducing translational barriers, thereby furthering the adoption of MMS.
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Affiliation(s)
- Tracey A Brereton
- Center for Digital Health, Mayo Clinic, Rochester, MN, United States
| | - Momin M Malik
- Center for Digital Health, Mayo Clinic, Rochester, MN, United States
| | - Mark Lifson
- Center for Digital Health, Mayo Clinic, Rochester, MN, United States
| | - Jason D Greenwood
- Department of Family Medicine, Mayo Clinic, Rochester, MN, United States
| | - Kevin J Peterson
- Center for Digital Health, Mayo Clinic, Rochester, MN, United States
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Furtado FS, Catalano OA. Editorial for "Automated MR Image Prescription of the Liver Using Deep Learning: Development, Evaluation, and Prospective Implementation". J Magn Reson Imaging 2022. [PMID: 36510405 DOI: 10.1002/jmri.28566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 11/02/2022] [Indexed: 12/15/2022] Open
Affiliation(s)
- Felipe S Furtado
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
| | - Onofrio A Catalano
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
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Leung T, Kulkarni V, Pant R, Kharat A. Levels of Autonomous Radiology. Interact J Med Res 2022; 11:e38655. [PMID: 36476422 PMCID: PMC9773033 DOI: 10.2196/38655] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 09/09/2022] [Accepted: 09/13/2022] [Indexed: 12/25/2022] Open
Abstract
Radiology, being one of the younger disciplines of medicine with a history of just over a century, has witnessed tremendous technological advancements and has revolutionized the way we practice medicine today. In the last few decades, medical imaging modalities have generated seismic amounts of medical data. The development and adoption of artificial intelligence applications using this data will lead to the next phase of evolution in radiology. It will include automating laborious manual tasks such as annotations, report generation, etc, along with the initial radiological assessment of patients and imaging features to aid radiologists in their diagnostic and treatment planning workflow. We propose a level-wise classification for the progression of automation in radiology, explaining artificial intelligence assistance at each level with the corresponding challenges and solutions. We hope that such discussions can help us address challenges in a structured way and take the necessary steps to ensure the smooth adoption of new technologies in radiology.
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Affiliation(s)
| | | | - Richa Pant
- DeepTek Medical Imaging Pvt Ltd, Pune, India
| | - Amit Kharat
- DeepTek Medical Imaging Pvt Ltd, Pune, India.,Dr DY Patil Hospital, DY Patil University, Pune, India
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Qu C, Zou Y, Dai Q, Ma Y, He J, Liu Q, Kuang W, Jia Z, Chen T, Gong Q. Advancing diagnostic performance and clinical applicability of deep learning-driven generative adversarial networks for Alzheimer's disease. PSYCHORADIOLOGY 2021; 1:225-248. [PMID: 38666217 PMCID: PMC10917234 DOI: 10.1093/psyrad/kkab017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/18/2021] [Accepted: 11/25/2021] [Indexed: 02/05/2023]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease that severely affects the activities of daily living in aged individuals, which typically needs to be diagnosed at an early stage. Generative adversarial networks (GANs) provide a new deep learning method that show good performance in image processing, while it remains to be verified whether a GAN brings benefit in AD diagnosis. The purpose of this research is to systematically review psychoradiological studies on the application of a GAN in the diagnosis of AD from the aspects of classification of AD state and AD-related image processing compared with other methods. In addition, we evaluated the research methodology and provided suggestions from the perspective of clinical application. Compared with other methods, a GAN has higher accuracy in the classification of AD state and better performance in AD-related image processing (e.g. image denoising and segmentation). Most studies used data from public databases but lacked clinical validation, and the process of quantitative assessment and comparison in these studies lacked clinicians' participation, which may have an impact on the improvement of generation effect and generalization ability of the GAN model. The application value of GANs in the classification of AD state and AD-related image processing has been confirmed in reviewed studies. Improvement methods toward better GAN architecture were also discussed in this paper. In sum, the present study demonstrated advancing diagnostic performance and clinical applicability of GAN for AD, and suggested that the future researchers should consider recruiting clinicians to compare the algorithm with clinician manual methods and evaluate the clinical effect of the algorithm.
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Affiliation(s)
- Changxing Qu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610044, China
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China School of Stomatology, Sichuan University, Chengdu 610044, China
| | - Yinxi Zou
- West China School of Medicine, Sichuan University, Chengdu 610044, China
| | - Qingyi Dai
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China School of Stomatology, Sichuan University, Chengdu 610044, China
| | - Yingqiao Ma
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610044, China
| | - Jinbo He
- School of Psychology, Central China Normal University, Wuhan 430079, China
| | - Qihong Liu
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Weihong Kuang
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu 610065, China
| | - Zhiyun Jia
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610044, China
| | - Taolin Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610044, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China
- Functional and Molecular Imaging Key Laboratory of Sichuan Provience, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610044, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China
- Functional and Molecular Imaging Key Laboratory of Sichuan Provience, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
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