Time to reality check the promises of machine learning-powered precision medicine

The Lancet Digital Health, Journal Year: 2020, Volume and Issue: 2(12), P. e677 - e680

Published: Sept. 16, 2020

Machine learning methods, combined with large electronic health databases, could enable a personalised approach to medicine through improved diagnosis and prediction of individual responses therapies. If successful, this strategy would represent revolution in clinical research practice. However, although the vision individually tailored is alluring, there need distinguish genuine potential from hype. We argue that goal medical care faces serious challenges, many which cannot be addressed algorithmic complexity, call for collaboration between traditional methodologists experts machine avoid extensive waste.

Language: Английский

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Deep learning and the electrocardiogram: review of the current state-of-the-art

EP Europace, Journal Year: 2020, Volume and Issue: 23(8), P. 1179 - 1191

Published: Nov. 26, 2020

Abstract In the recent decade, deep learning, a subset of artificial intelligence and machine has been used to identify patterns in big healthcare datasets for disease phenotyping, event predictions, complex decision making. Public electrocardiograms (ECGs) have existed since 1980s very specific tasks cardiology, such as arrhythmia, ischemia, cardiomyopathy detection. Recently, private institutions begun curating large ECG databases that are orders magnitude larger than public ingestion by learning models. These efforts demonstrated not only improved performance generalizability these aforementioned but also application novel clinical scenarios. This review focuses on orienting clinician towards fundamental tenets state-of-the-art prior its use analysis, current applications ECGs, well their limitations future areas improvement.

Language: Английский

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SHIFTing artificial intelligence to be responsible in healthcare: A systematic review

Social Science & Medicine, Journal Year: 2022, Volume and Issue: 296, P. 114782 - 114782

Published: Feb. 4, 2022

A variety of ethical concerns about artificial intelligence (AI) implementation in healthcare have emerged as AI becomes increasingly applicable and technologically advanced. The last decade has witnessed significant endeavors striking a balance between considerations health transformation led by AI. Despite growing interest ethics, implementing AI-related technologies initiatives responsibly settings remains challenge. In response to this topical challenge, we reviewed 253 articles pertaining ethics published 2000 2020, summarizing the coherent themes responsible initiatives. preferred reporting items for systematic review meta-analysis (PRISMA) approach was employed screen select articles, hermeneutic adopted conduct literature review. By synthesizing relevant knowledge from governance propose initiative framework that encompasses five core solution developers, professionals, policy makers. These are summarized acronym SHIFT: Sustainability, Human centeredness, Inclusiveness, Fairness, Transparency. addition, unravel key issues challenges concerning use healthcare, outline avenues future research.

Language: Английский

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Methodological standards for the development and evaluation of clinical prediction rules: a review of the literature

Diagnostic and Prognostic Research, Journal Year: 2019, Volume and Issue: 3(1)

Published: Aug. 21, 2019

Clinical prediction rules (CPRs) that predict the absolute risk of a clinical condition or future outcome for individual patients are abundant in medical literature; however, systematic reviews have demonstrated shortcomings methodological quality and reporting studies. To maximise potential usefulness CPRs, they must be rigorously developed validated, their impact on practice patient outcomes evaluated. This review aims to present comprehensive overview stages involved development, validation evaluation describe detail standards required at each stage, illustrated with examples where appropriate. Important features study design, statistical analysis, modelling strategy, data collection, performance assessment, CPR presentation discussed, addition other, often overlooked aspects such as acceptability, cost-effectiveness longer-term implementation comparison judgement. Although development robust, clinically useful is anything but straightforward, adherence plethora standards, recommendations frameworks stage will assist rigorous has contribute usefully decision-making positive care.

Language: Английский

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Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

BMJ, Journal Year: 2022, Volume and Issue: unknown, P. e070904 - e070904

Published: May 18, 2022

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, silico, evaluation, but few have yet demonstrated real benefit to patient care. Early stage evaluation is important assess an AI system’s actual at small scale, ensure its safety, evaluate the human factors surrounding use, and pave way further large scale trials. However, reporting these early studies remains inadequate. The present statement provides a multistakeholder, consensus-based guideline for Developmental Exploratory Clinical Investigations DEcision driven by Artificial Intelligence (DECIDE-AI). We conducted two round, modified Delphi process collect analyse expert opinion on systems. Experts were recruited from 20 predefined stakeholder categories. final composition wording was determined virtual consensus meeting. checklist Explanation & Elaboration (E&E) sections refined based feedback qualitative process. 123 experts participated first round Delphi, 138 second, 16 meeting, evaluation. DECIDE-AI comprises 17 specific items (made 28 subitems) 10 generic items, with E&E paragraph provided each. Through consultation range stakeholders, we developed comprising key that should be reported AI-based healthcare. By providing actionable minimal will facilitate appraisal replicability their findings.

Language: Английский

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A time-resolved proteomic and prognostic map of COVID-19

Cell Systems, Journal Year: 2021, Volume and Issue: 12(8), P. 780 - 794.e7

Published: June 14, 2021

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of disease 139 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts enzyme activities, well untargeted plasma proteomes at 687 sampling points. report an initial spike a systemic inflammatory response, which gradually alleviated followed protein signature indicative tissue repair, metabolic reconstitution, immunomodulation. identify prognostic marker signatures for devising risk-adapted treatment strategies use machine learning classify therapeutic needs. show that models based on proteome are transferable independent cohort. Our study presents map linking routinely used parameters their dynamics infectious disease.

Language: Английский

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Randomized Clinical Trials of Machine Learning Interventions in Health Care

JAMA Network Open, Journal Year: 2022, Volume and Issue: 5(9), P. e2233946 - e2233946

Published: Sept. 29, 2022

Importance Despite the potential of machine learning to improve multiple aspects patient care, barriers clinical adoption remain. Randomized trials (RCTs) are often a prerequisite large-scale an intervention, and important questions remain regarding how interventions being incorporated into in health care. Objective To systematically examine design, reporting standards, risk bias, inclusivity RCTs for medical interventions. Evidence Review In this systematic review, Cochrane Library, Google Scholar, Ovid Embase, MEDLINE, PubMed, Scopus, Web Science Core Collection online databases were searched citation chasing was done find relevant articles published from inception each database October 15, 2021. Search terms learning, decision-making, used. Exclusion criteria included implementation non-RCT absence original data, evaluation nonclinical Data extracted articles. Trial characteristics, including primary demographics, adherence CONSORT-AI guideline, bias analyzed. Findings Literature search yielded 19 737 articles, which 41 involved median 294 participants (range, 17-2488 participants). A total 16 RCTS (39%) 2021, 21 (51%) conducted at single sites, 15 (37%) endoscopy. No adhered all standards. Common reasons nonadherence not assessing poor-quality or unavailable input data (38 [93%]), analyzing performance errors statement code algorithm availability (37 [90%]). Overall high 7 (17%). Of 11 (27%) that reported race ethnicity proportion underrepresented minority groups 21% 0%-51%). Conclusions Relevance This review found despite large number learning–based algorithms development, few these technologies have been conducted. Among RCTs, there variability standards lack groups. These findings merit attention should be considered future RCT design reporting.

Language: Английский

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Artificial Intelligence Solutions to Increase Medication Adherence in Patients With Non-communicable Diseases

Frontiers in Digital Health, Journal Year: 2021, Volume and Issue: 3

Published: June 29, 2021

Artificial intelligence (AI) tools are increasingly being used within healthcare for various purposes, including helping patients to adhere drug regimens. The aim of this narrative review was describe: (1) studies on AI that can be measure and increase medication adherence in with non-communicable diseases (NCDs); (2) the benefits using these purposes; (3) challenges use healthcare; (4) priorities future research. We discuss current technologies, mobile phone applications, reminder systems, patient empowerment, instruments integrated care, machine learning. may key understanding complex interplay factors underly non-adherence NCD patients. AI-assisted interventions aiming improve communication between physicians, monitor consumption, empower patients, ultimately, levels lead better clinical outcomes quality life However, is challenged by numerous factors; characteristics users impact effectiveness an tool, which further inequalities healthcare, there concerns it could depersonalize medicine. success widespread technologies will depend data storage capacity, processing power, other infrastructure capacities systems. Research needed evaluate solutions different groups establish barriers adoption, especially light COVID-19 pandemic, has led a rapid development digital health technologies.

Language: Английский

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Digital Mental Health Challenges and the Horizon Ahead for Solutions

JMIR Mental Health, Journal Year: 2021, Volume and Issue: 8(3), P. e26811 - e26811

Published: Feb. 27, 2021

The demand outstripping supply of mental health resources during the COVID-19 pandemic presents opportunities for digital technology tools to fill this new gap and, in process, demonstrate capabilities increase their effectiveness and efficiency. However, technology-enabled services have faced challenges being sustainably implemented despite showing promising outcomes efficacy trials since early 2000s. ongoing failure these implementations has been addressed reconceptualized models frameworks, along with various efforts branch out among disparate developers clinical researchers provide them a key furthering evaluative research. limitations traditional research methods dealing complexities care warrant diversified approach. crux implementation is evaluation existing studies. Web-based interventions are increasingly used pandemic, allowing affordable access psychological therapies. lagging infrastructure skill base limited application solutions care. Methodologies need be converged owing rapid development technologies that outpaced rigorous strategies prevent illness. functions implications human-computer interaction require better understanding overcome engagement barriers, especially predictive technologies. Explainable artificial intelligence incorporated into obtain positive responsible outcomes. Investment platforms associated apps real-time screening, tracking, treatment offer promise cost-effectiveness vulnerable populations. Although machine learning by study conduct reporting methods, increasing use unstructured data strengthened its potential. Early evidence suggests advantages outweigh disadvantages incrementing such technology. an evidence-based approach integration decision support guide policymakers implementation. There complex range issues effectiveness, equity, access, ethics (eg, privacy, confidentiality, fairness, transparency, reproducibility, accountability), which resolution. Evidence-informed policies, eminent products services, skills maintain required. Studies focus on developing explainable intelligence–based enhance resilience decisions practitioners. Investments should ensure safety workability. End users encourage innovative effectively evaluate render worthwhile investment. Technology-enabled hybrid model most likely effective specialists using vulnerable, at-risk populations but not severe cases ill health).

Language: Английский

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The value of standards for health datasets in artificial intelligence-based applications

Nature Medicine, Journal Year: 2023, Volume and Issue: 29(11), P. 2929 - 2938

Published: Oct. 26, 2023

Abstract Artificial intelligence as a medical device is increasingly being applied to healthcare for diagnosis, risk stratification and resource allocation. However, growing body of evidence has highlighted the algorithmic bias, which may perpetuate existing health inequity. This problem arises in part because systemic inequalities dataset curation, unequal opportunity participate research access. study aims explore standards, frameworks best practices ensuring adequate data diversity datasets. Exploring literature expert views an important step towards development consensus-based guidelines. The comprises two parts: systematic review datasets; survey thematic analysis stakeholder equity artificial device. We found that need was well described literature, experts generally favored robust set guidelines, but there were mixed about how these could be implemented practically. outputs this will used inform standards transparency datasets (the STANDING Together initiative).

Language: Английский

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