scholarly journals MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care

2020 ◽  
Vol 27 (12) ◽  
pp. 2011-2015 ◽  
Author(s):  
Tina Hernandez-Boussard ◽  
Selen Bozkurt ◽  
John P A Ioannidis ◽  
Nigam H Shah
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Clinical Decision ◽  
Digital Data ◽  
Reporting Standards ◽  
Minimum Information ◽  
Support Tools ◽  
Hidden Biases

Abstract The rise of digital data and computing power have contributed to significant advancements in artificial intelligence (AI), leading to the use of classification and prediction models in health care to enhance clinical decision-making for diagnosis, treatment and prognosis. However, such advances are limited by the lack of reporting standards for the data used to develop those models, the model architecture, and the model evaluation and validation processes. Here, we present MINIMAR (MINimum Information for Medical AI Reporting), a proposal describing the minimum information necessary to understand intended predictions, target populations, and hidden biases, and the ability to generalize these emerging technologies. We call for a standard to accurately and responsibly report on AI in health care. This will facilitate the design and implementation of these models and promote the development and use of associated clinical decision support tools, as well as manage concerns regarding accuracy and bias.

scholarly journals Introducing Artificial Intelligence Training in Medical Education

10.2196/16048 ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. e16048 ◽  
Author(s):  
Ketan Paranjape ◽  
Michiel Schinkel ◽  
Rishi Nannan Panday ◽  
Josip Car ◽  
Prabath Nanayakkara
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Medical Education ◽  
Health Care Professionals ◽  
Access To Health Care ◽  
Clinical Decision Making ◽  
New Technology ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Use Of Data

Health care is evolving and with it the need to reform medical education. As the practice of medicine enters the age of artificial intelligence (AI), the use of data to improve clinical decision making will grow, pushing the need for skillful medicine-machine interaction. As the rate of medical knowledge grows, technologies such as AI are needed to enable health care professionals to effectively use this knowledge to practice medicine. Medical professionals need to be adequately trained in this new technology, its advantages to improve cost, quality, and access to health care, and its shortfalls such as transparency and liability. AI needs to be seamlessly integrated across different aspects of the curriculum. In this paper, we have addressed the state of medical education at present and have recommended a framework on how to evolve the medical education curriculum to include AI.

Artificial Intelligence in Lung Cancer: Bridging the Gap Between Computational Power and Clinical Decision-Making

2020 ◽  
pp. 084653712094143
Author(s):  
Jaryd R. Christie ◽  
Pencilla Lang ◽  
Lauren M. Zelko ◽  
David A. Palma ◽  
Mohamed Abdelrazek ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Biomarker Discovery ◽  
Surgical Techniques ◽  
Clinical Decision ◽  
Clinical Implementation ◽  
Cancer Management

Lung cancer remains the most common cause of cancer death worldwide. Recent advances in lung cancer screening, radiotherapy, surgical techniques, and systemic therapy have led to increasing complexity in diagnosis, treatment decision-making, and assessment of recurrence. Artificial intelligence (AI)–based prediction models are being developed to address these issues and may have a future role in screening, diagnosis, treatment selection, and decision-making around salvage therapy. Imaging plays an essential role in all components of lung cancer management and has the potential to play a key role in AI applications. Artificial intelligence has demonstrated value in prognostic biomarker discovery in lung cancer diagnosis, treatment, and response assessment, putting it at the forefront of the next phase of personalized medicine. However, although exploratory studies demonstrate potential utility, there is a need for rigorous validation and standardization before AI can be utilized in clinical decision-making. In this review, we will provide a summary of the current literature implementing AI for outcome prediction in lung cancer. We will describe the anticipated impact of AI on the management of patients with lung cancer and discuss the challenges of clinical implementation of these techniques.

scholarly journals Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming?

2022 ◽  
Vol 8 ◽  
Author(s):  
Anastasia Fotaki ◽  
Esther Puyol-Antón ◽  
Amedeo Chiribiri ◽  
René Botnar ◽  
Kuberan Pushparajah ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Wide Spectrum ◽  
Clinical Decision ◽  
Training Data ◽  
Response To Treatment ◽  
Cine Imaging ◽  
Post Processing

Artificial intelligence (AI) refers to the area of knowledge that develops computerised models to perform tasks that typically require human intelligence. These algorithms are programmed to learn and identify patterns from “training data,” that can be subsequently applied to new datasets, without being explicitly programmed to do so. AI is revolutionising the field of medical imaging and in particular of Cardiovascular Magnetic Resonance (CMR) by providing deep learning solutions for image acquisition, reconstruction and analysis, ultimately supporting the clinical decision making. Numerous methods have been developed over recent years to enhance and expedite CMR data acquisition, image reconstruction, post-processing and analysis; along with the development of promising AI-based biomarkers for a wide spectrum of cardiac conditions. The exponential rise in the availability and complexity of CMR data has fostered the development of different AI models. Integration in clinical routine in a meaningful way remains a challenge. Currently, innovations in this field are still mostly presented in proof-of-concept studies with emphasis on the engineering solutions; often recruiting small patient cohorts or relying on standardised databases such as Multi-ethnic Study on atherosclerosis (MESA), UK Biobank and others. The wider incorporation of clinically valid endpoints such as symptoms, survival, need and response to treatment remains to be seen. This review briefly summarises the current principles of AI employed in CMR and explores the relevant prospective observational studies in cardiology patient cohorts. It provides an overview of clinical studies employing undersampled reconstruction techniques to speed up the scan encompassing cine imaging, whole-heart imaging, multi-parametric mapping and magnetic resonance fingerprinting along with the clinical utility of AI applications in image post-processing, and analysis. Specific focus is given to studies that have incorporated CMR-derived prediction models for prognostication in cardiac disease. It also discusses current limitations and proposes potential developments to enable multi-disciplinary collaboration for improved evidence-based medicine. AI is an extremely promising field and the timely integration of clinician's input in the ingenious technical investigator's paradigm holds promise for a bright future in the medical field.

scholarly journals Introducing Artificial Intelligence Training in Medical Education (Preprint)

2019 ◽  
Author(s):  
Ketan Paranjape ◽  
Michiel Schinkel ◽  
Rishi Nannan Panday ◽  
Josip Car ◽  
Prabath Nanayakkara
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Medical Education ◽  
Health Care Professionals ◽  
Access To Health Care ◽  
Clinical Decision Making ◽  
New Technology ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Use Of Data

UNSTRUCTURED Health care is evolving and with it the need to reform medical education. As the practice of medicine enters the age of artificial intelligence (AI), the use of data to improve clinical decision making will grow, pushing the need for skillful medicine-machine interaction. As the rate of medical knowledge grows, technologies such as AI are needed to enable health care professionals to effectively use this knowledge to practice medicine. Medical professionals need to be adequately trained in this new technology, its advantages to improve cost, quality, and access to health care, and its shortfalls such as transparency and liability. AI needs to be seamlessly integrated across different aspects of the curriculum. In this paper, we have addressed the state of medical education at present and have recommended a framework on how to evolve the medical education curriculum to include AI.

scholarly journals Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19

2020 ◽  
Author(s):  
Eliane Röösli ◽  
Brian Rice ◽  
Tina Hernandez-Boussard
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Health Disparities ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Mitigation Strategies ◽  
Reporting Standards ◽  
Regulatory Frameworks ◽  
Rapid Dissemination ◽  
Data Source

Abstract The COVID-19 pandemic is presenting a disproportionate impact on minorities in terms of infection rate, hospitalizations, and mortality. Many believe artificial intelligence (AI) is a solution to guide clinical decision-making for this novel disease, resulting in the rapid dissemination of underdeveloped and potentially biased models, which may exacerbate the disparities gap. We believe there is an urgent need to enforce the systematic use of reporting standards and develop regulatory frameworks for a shared COVID-19 data source to address the challenges of bias in AI during this pandemic. There is hope that AI can help guide treatment decisions within this crisis; yet given the pervasiveness of biases, a failure to proactively develop comprehensive mitigation strategies during the COVID-19 pandemic risks exacerbating existing health disparities.

scholarly journals The Impact of Artificial Intelligence on Quality and Safety

2020 ◽  
Vol 10 (1_suppl) ◽  
pp. 99S-103S
Author(s):  
Michelle S. Lee ◽  
Matthew M. Grabowski ◽  
Ghaith Habboub ◽  
Thomas E. Mroz
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Health Care Quality ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Care Quality ◽  
Cost Of Care ◽  
Quality And Safety ◽  
Future Outcomes ◽  
The Impact

As exponential expansion of computing capacity converges with unsustainable health care spending, a hopeful opportunity has emerged: the use of artificial intelligence to enhance health care quality and safety. These computer-based algorithms can perform the intricate and extremely complex mathematical operations of classification or regression on immense amounts of data to detect intricate and potentially previously unknown patterns in that data, with the end result of creating predictive models that can be utilized in clinical practice. Such models are designed to distinguish relevant from irrelevant data regarding a particular patient; choose appropriate perioperative care, intervention or surgery; predict cost of care and reimbursement; and predict future outcomes on a variety of anchored measures. If and when one is brought to fruition, an artificial intelligence platform could serve as the first legitimate clinical decision-making tool in spine care, delivering on the value equation while serving as a source for improving physician performance and promoting appropriate, efficient care in this era of financial uncertainty in health care.

What Is Medical Ethics Consultation?

2008 ◽  
Vol 36 (1) ◽  
pp. 95-118 ◽  
Author(s):  
Giles R. Scofield
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Medical Ethics ◽  
Medical Technology ◽  
Clinical Decision Making ◽  
Ethics Consultation ◽  
Clinical Decision ◽  
Law Students ◽  
Medical Ethicist ◽  
Care Decision

As everybody knows, advances in medicine and medical technology have brought enormous benefits to, and created vexing choices for, us all – choices that can, and occasionally do, test the very limits of thinking itself. As everyone also knows, we live in the age of consultants, i.e., of professional experts who are ready, willing, and able to give us advice on any and every conceivable question. One such consultant is the medical ethics consultant, or the medical ethicist who consults.Medical ethics consultants involve themselves in just about every aspect of health care decision making. They help legislators and judges determine law, hospitals formulate policies, medical schools develop curricula, etc. In addition to educating physicians, nurses, and lawyers, amongst others, including medical, nursing, and law students, they participate in clinical decision making at the bedside.

Consent, Refusal, and Waivers in Patient-Centered Dysphagia Care: Using Law, Ethics, and Evidence to Guide Clinical Practice

2016 ◽  
Vol 25 (4) ◽  
pp. 453-469 ◽  
Author(s):  
Jennifer Horner ◽  
Maria Modayil ◽  
Laura Roche Chapman ◽  
An Dinh
Keyword(s):  
Health Care ◽  
Public Policy ◽  
Clinical Practice ◽  
Health Care Providers ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Care Providers ◽  
Patient Centered ◽  
Legal Advice ◽  
Medical Context

PurposeWhen patients refuse medical or rehabilitation procedures, waivers of liability have been used to bar future lawsuits. The purpose of this tutorial is to review the myriad issues surrounding consent, refusal, and waivers. The larger goal is to invigorate clinical practice by providing clinicians with knowledge of ethics and law. This tutorial is for educational purposes only and does not constitute legal advice.MethodThe authors use a hypothetical case of a “noncompliant” individual under the care of an interdisciplinary neurorehabilitation team to illuminate the ethical and legal features of the patient–practitioner relationship; the elements of clinical decision-making capacity; the duty of disclosure and the right of informed consent or informed refusal; and the relationship among noncompliance, defensive practices, and iatrogenic harm. We explore the legal question of whether waivers of liability in the medical context are enforceable or unenforceable as a matter of public policy.ConclusionsSpeech-language pathologists, among other health care providers, have fiduciary and other ethical and legal obligations to patients. Because waivers try to shift liability for substandard care from health care providers to patients, courts usually find waivers of liability in the medical context unenforceable as a matter of public policy.

HEALTH TECHNOLOGY ASSESSMENT AND CLINICAL DECISION MAKING:

1999 ◽  
Vol 15 (3) ◽  
pp. 585-592 ◽  
Author(s):  
Alicia Granados
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Health Technology Assessment ◽  
Case Studies ◽  
Technology Assessment ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Health Technology ◽  
Evidence Based ◽  
Systematic Process

This paper examines the rationality of the concepts underlying evidence—based medicineand health technology assessment (HTA), which are part of a new current aimed at promoting the use of the results of scientific studies for decision making in health care. It describes the different approaches and purposes of this worldwide movement, in relation to clinical decision making, through a summarized set of specific HTA case studies from Catalonia, Spain. The examples illustrate how the systematic process of HTA can help in several types of uncertainties related to clinical decision making.

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