Assessing the Economic Value of Clinical Artificial Intelligence: Challenges and Opportunities

Hendrix, Nathaniel; Veenstra, David L.; Cheng, Mindy M.; Anderson, Nicholas C.; Verguet, Stéphane

doi:10.1016/j.jval.2021.08.015

Cited by 28 publications

(23 citation statements)

References 69 publications

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“…In addition, an algorithm applied in one environment will not automatically be suitable in another environment, requiring careful development, testing, and evaluation in each new context before implementing AI systems for patient care [127]. Intense debate has also arisen from the fact that AI studies tend to compare algorithm performances to clinicians when, instead, realistic applications would involve a combination of human and artificial intelligence [128]. This has raised concerns on whether human clinicians will become redundant with the advancements of AI technology or lose the skills they do not regularly use.…”

Section: Discussionmentioning

confidence: 99%

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Clinical Applications of Artificial Intelligence—An Updated Overview

Busnatu

Niculescu

Bolocan

et al. 2022

JCM

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Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.

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Section: Discussionmentioning

confidence: 99%

“…Moreover, automation bias means that humans tend to agree with AI decisions, even when they are incorrect. However, as machines cannot be held responsible for their decisions, the legal liability will still be on the shoulders of physicians [10,127,128].…”

Section: Discussionmentioning

confidence: 99%

Clinical Applications of Artificial Intelligence—An Updated Overview

Busnatu

Niculescu

Bolocan

et al. 2022

JCM

View full text Add to dashboard Cite

show abstract

“…Specifically, the cost-effectiveness ratio, which is the main outcome of the health technology assessment methodology, represents the primary metrics and consists of summed incremental health outcomes divided by the incremental costs associated with using the intervention under consideration. 94 However, acquiring data on AI-associated health outcomes is challenging. As most evidence on clinical AI performance comes from retrospective studies, AI performance is often compared with clinician performance unrealistically, and the effects of AI on clinician productivity are uncertain.…”

Section: Must Have Qualitiesmentioning

confidence: 99%

Must-have Qualities of Clinical Research on Artificial Intelligence and Machine Learning

Koçak¹,

Cuocolo²,

Santos³

et al. 2023

Balkan Med J

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In the field of computer science, known as artificial intelligence, algorithms imitate reasoning tasks that are typically performed by humans. The techniques that allow machines to learn and get better at tasks such as recognition and prediction, which form the basis of clinical practice, are referred to as machine learning, which is a subfield of artificial intelligence. The number of artificial intelligence-and machine learnings-related publications in clinical journals has grown exponentially, driven by recent developments in computation and the accessibility of simple tools. However, clinicians are often not included in data science teams, which may limit the clinical relevance, explanability, workflow compatibility, and quality improvement of artificial intelligence solutions. Thus, this results in the language barrier between clinicians and artificial intelligence developers. Healthcare practitioners sometimes lack a basic understanding of artificial intelligence research because the approach is difficult for non-specialists to understand. Furthermore, many editors and reviewers of medical publications might not be familiar with the fundamental ideas behind these technologies, which may prevent journals from publishing high-quality artificial intelligence studies or, worse still, could allow for the publication of low-quality works. In this review, we aim to improve readers’ artificial intelligence literacy and critical thinking. As a result, we concentrated on what we consider the 10 most important qualities of artificial intelligence research: valid scientific purpose, high-quality data set, robust reference standard, robust input, no information leakage, optimal bias-variance tradeoff, proper model evaluation, proven clinical utility, transparent reporting, and open science. Before designing a study, one should have defined a sound scientific purpose. Then, it should be backed by a high-quality data set, robust input, and a solid reference standard. The artificial intelligence development pipeline should prevent information leakage. For the models, optimal bias-variance tradeoff should be achieved, and generalizability assessment must be adequately performed. The clinical value of the final models must also be established. After the study, thought should be given to transparency in publishing the process and results as well as open science for sharing data, code, and models. We hope this work may improve the artificial intelligence literacy and mindset of the readers.

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“…Thus, clinicians’ productivity is likely to improve when AI is used. If poorly implemented though, AI may also cause clinicians’ workload even to increase ( 87 ). Proper implementation will thus be a central aspect when developing and integrating novel AI tools into the clinical workflow.…”

Section: Economic Aspectsmentioning

confidence: 99%

Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective

Baeßler

Götz

Antoniades

et al. 2023

Front. Cardiovasc. Med.

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Coronary computed tomography angiography (CCTA) is increasingly the cornerstone in the management of patients with chronic coronary syndromes. This fact is reflected by current guidelines, which show a fundamental shift towards non-invasive imaging - especially CCTA. The guidelines for acute and stable coronary artery disease (CAD) of the European Society of Cardiology from 2019 and 2020 emphasize this shift. However, to fulfill this new role, a broader availability in adjunct with increased robustness of data acquisition and speed of data reporting of CCTA is needed. Artificial intelligence (AI) has made enormous progress for all imaging methodologies concerning (semi)-automatic tools for data acquisition and data post-processing, with outreach toward decision support systems. Besides onco- and neuroimaging, cardiac imaging is one of the main areas of application. Most current AI developments in the scenario of cardiac imaging are related to data postprocessing. However, AI applications (including radiomics) for CCTA also should enclose data acquisition (especially the fact of dose reduction) and data interpretation (presence and extent of CAD). The main effort will be to integrate these AI-driven processes into the clinical workflow, and to combine imaging data/results with further clinical data, thus - beyond the diagnosis of CAD- enabling prediction and forecast of morbidity and mortality. Furthermore, data fusing for therapy planning (e.g., invasive angiography/TAVI planning) will be warranted. The aim of this review is to present a holistic overview of AI applications in CCTA (including radiomics) under the umbrella of clinical workflows and clinical decision-making. The review first summarizes and analyzes applications for the main role of CCTA, i.e., to non-invasively rule out stable coronary artery disease. In the second step, AI applications for additional diagnostic purposes, i.e., to improve diagnostic power (CAC = coronary artery classifications), improve differential diagnosis (CT-FFR and CT perfusion), and finally improve prognosis (again CAC plus epi- and pericardial fat analysis) are reviewed.

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Assessing the Economic Value of Clinical Artificial Intelligence: Challenges and Opportunities

Cited by 28 publications

References 69 publications

Clinical Applications of Artificial Intelligence—An Updated Overview

Clinical Applications of Artificial Intelligence—An Updated Overview

Must-have Qualities of Clinical Research on Artificial Intelligence and Machine Learning

Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective

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