Big Data and Machine Learning in Health Care Beam, Andrew L; Kohane, Isaac S
JAMA : the journal of the American Medical Association,
04/2018, Volume:
319, Issue:
13
Journal Article
Peer reviewed
The article discusses the impact that big data and machine learning is having on health care. Some of the different ways in which machine learning can be applied to health care are highlighted. The ...use of big data in the health care sector is included.
The unique challenges and obstacles to reproducibility presented by machine learning models as clinical prediction tools are discussed. Careful consideration is necessary to ensure that these ...approaches are valid and deployed safely and effectively. In view of this need, practices and standards to improve the models' replicability are also suggested.
The black-box nature of current artificial intelligence (AI) has caused some to question whether AI must be explainable to be used in high-stakes scenarios such as medicine. It has been argued that ...explainable AI will engender trust with the health-care workforce, provide transparency into the AI decision making process, and potentially mitigate various kinds of bias. In this Viewpoint, we argue that this argument represents a false hope for explainable AI and that current explainability methods are unlikely to achieve these goals for patient-level decision support. We provide an overview of current explainability techniques and highlight how various failure cases can cause problems for decision making for individual patients. In the absence of suitable explainability methods, we advocate for rigorous internal and external validation of AI models as a more direct means of achieving the goals often associated with explainability, and we caution against having explainability be a requirement for clinically deployed models.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
There is great excitement that medical artificial intelligence (AI) based on machine learning (ML) can be used to improve decision making at the patient level in a variety of healthcare settings. ...However, the quantification and communication of uncertainty for individual predictions is often neglected even though uncertainty estimates could lead to more principled decision-making and enable machine learning models to automatically or semi-automatically abstain on samples for which there is high uncertainty. In this article, we provide an overview of different approaches to uncertainty quantification and abstention for machine learning and highlight how these techniques could improve the safety and reliability of current ML systems being used in healthcare settings. Effective quantification and communication of uncertainty could help to engender trust with healthcare workers, while providing safeguards against known failure modes of current machine learning approaches. As machine learning becomes further integrated into healthcare environments, the ability to say "I'm not sure" or "I don't know" when uncertain is a necessary capability to enable safe clinical deployment.
Adversarial attacks on medical machine learning Finlayson, Samuel G; Bowers, John D; Ito, Joichi ...
Science (American Association for the Advancement of Science),
03/2019, Volume:
363, Issue:
6433
Journal Article
Peer reviewed
Open access
Emerging vulnerabilities demand new conversations
With public and academic attention increasingly focused on the new role of machine learning in the health information economy, an unusual and ...no-longer-esoteric category of vulnerabilities in machine-learning systems could prove important. These vulnerabilities allow a small, carefully designed change in how inputs are presented to a system to completely alter its output, causing it to confidently arrive at manifestly wrong conclusions. These advanced techniques to subvert otherwise-reliable machine-learning systems—so-called adversarial attacks—have, to date, been of interest primarily to computer science researchers (
1
). However, the landscape of often-competing interests within health care, and billions of dollars at stake in systems' outputs, implies considerable problems. We outline motivations that various players in the health care system may have to use adversarial attacks and begin a discussion of what to do about them. Far from discouraging continued innovation with medical machine learning, we call for active engagement of medical, technical, legal, and ethical experts in pursuit of efficient, broadly available, and effective health care that machine learning will enable.
IntroductionThe Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were ...both published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to develop an extension to the TRIPOD statement (TRIPOD-artificial intelligence, AI) and the PROBAST (PROBAST-AI) tool for prediction model studies that applied machine learning techniques.Methods and analysisTRIPOD-AI and PROBAST-AI will be developed following published guidance from the EQUATOR Network, and will comprise five stages. Stage 1 will comprise two systematic reviews (across all medical fields and specifically in oncology) to examine the quality of reporting in published machine-learning-based prediction model studies. In stage 2, we will consult a diverse group of key stakeholders using a Delphi process to identify items to be considered for inclusion in TRIPOD-AI and PROBAST-AI. Stage 3 will be virtual consensus meetings to consolidate and prioritise key items to be included in TRIPOD-AI and PROBAST-AI. Stage 4 will involve developing the TRIPOD-AI checklist and the PROBAST-AI tool, and writing the accompanying explanation and elaboration papers. In the final stage, stage 5, we will disseminate TRIPOD-AI and PROBAST-AI via journals, conferences, blogs, websites (including TRIPOD, PROBAST and EQUATOR Network) and social media. TRIPOD-AI will provide researchers working on prediction model studies based on machine learning with a reporting guideline that can help them report key details that readers need to evaluate the study quality and interpret its findings, potentially reducing research waste. We anticipate PROBAST-AI will help researchers, clinicians, systematic reviewers and policymakers critically appraise the design, conduct and analysis of machine learning based prediction model studies, with a robust standardised tool for bias evaluation.Ethics and disseminationEthical approval has been granted by the Central University Research Ethics Committee, University of Oxford on 10-December-2020 (R73034/RE001). Findings from this study will be disseminated through peer-review publications.PROSPERO registration numberCRD42019140361 and CRD42019161764.
Artificial intelligence has become a frequent topic in the news cycle, with reports of breakthroughs in speech recognition, computer vision, and textual understanding that have made their way into a ...bevy of products and services that are used every day. A modern graphics processing unit costing approximately $1000 can be added to most existing computer systems with little difficulty and can process about 3000 images per second depending on the complexity of the underlying deep learning model.The commercial efforts to push the technology into clinical care are becoming apparent, as several companies have begun to translate the research advancements to commercial applications.
Artificial intelligence in healthcare Yu, Kun-Hsing; Beam, Andrew L; Kohane, Isaac S
Nature biomedical engineering,
10/2018, Volume:
2, Issue:
10
Journal Article
Peer reviewed
Artificial intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding ...into areas that were previously thought to be only the province of human experts. In this Review Article, we outline recent breakthroughs in AI technologies and their biomedical applications, identify the challenges for further progress in medical AI systems, and summarize the economic, legal and social implications of AI in healthcare.
This Viewpoint describes the false dichotomy between statistics and machine learning and suggests considerations in building and evaluating clinical prediction models.
Artificial Intelligence in Medicine Beam, Andrew L.; Drazen, Jeffrey M.; Kohane, Isaac S. ...
The New England journal of medicine,
03/2023, Volume:
388, Issue:
13
Journal Article
Peer reviewed
Open access
The editors announce both a series of articles focusing on AI and machine learning in health care and the 2024 launch of a new journal,
NEJM AI
, a forum for evidence, resource sharing, and ...discussion of the possibilities and limitations of medical AI.
PDF
