The importance of being external. methodological insights for the external validation of machine learning models in medicine

Computer Methods and Programs in Biomedicine, Journal Year: 2021, Volume and Issue: 208, P. 106288 - 106288

Published: July 22, 2021

Language: Английский

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Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review

npj Digital Medicine, Journal Year: 2022, Volume and Issue: 5(1)

Published: Oct. 19, 2022

Abstract Transparency in Machine Learning (ML), often also referred to as interpretability or explainability, attempts reveal the working mechanisms of complex models. From a human-centered design perspective, transparency is not property ML model but an affordance, i.e., relationship between algorithm and users. Thus, prototyping user evaluations are critical attaining solutions that afford transparency. Following principles highly specialized high stakes domains, such medical image analysis, challenging due limited access end users knowledge imbalance those designers. To investigate state transparent we conducted systematic review literature from 2012 2021 PubMed, EMBASE, Compendex databases. We identified 2508 records 68 articles met inclusion criteria. Current techniques dominated by computational feasibility barely consider users, e.g. clinical stakeholders. Despite different roles developers no study reported formative research inform development Only few studies validated claims through empirical evaluations. These shortcomings put contemporary on at risk being incomprehensible thus, clinically irrelevant. alleviate these forthcoming research, introduce INTRPRT guideline , directive for systems analysis. The suggests principles, recommending first step understand needs domain requirements. guidelines increases likelihood algorithms enable stakeholders capitalize benefits ML.

Language: Английский

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Evaluation framework to guide implementation of AI systems into healthcare settings

BMJ Health & Care Informatics, Journal Year: 2021, Volume and Issue: 28(1), P. e100444 - e100444

Published: Oct. 1, 2021

To date, many artificial intelligence (AI) systems have been developed in healthcare, but adoption has limited. This may be due to inappropriate or incomplete evaluation and a lack of internationally recognised AI standards on evaluation. confidence the generalisability healthcare enable their integration into workflows, there is need for practical yet comprehensive instrument assess translational aspects available systems. Currently frameworks focus reporting regulatory little guidance regarding assessment like functional, utility ethical components.To address this gap create framework that assesses real-world systems, an international team translationally focused termed 'Translational Evaluation Healthcare (TEHAI)'. A critical review literature assessed existing gaps. Next, using health technology principles, components were identified consideration. These independently reviewed consensus inclusion final by panel eight expert.TEHAI includes three main components: capability, adoption. The emphasis features model development deployment distinguishes TEHAI from other instruments. In specific, can applied at any stage system.One major limitation narrow focus. TEHAI, because its strong foundation translation research models safety, value generalisability, not only theoretical basis also application assessing systems.The theoretic approach used develop should see it having just clinical settings, more broadly guide working

Language: Английский

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Guidelines for Artificial Intelligence in Medicine: Literature Review and Content Analysis of Frameworks

Journal of Medical Internet Research, Journal Year: 2022, Volume and Issue: 24(8), P. e36823 - e36823

Published: July 14, 2022

Artificial intelligence (AI) is rapidly expanding in medicine despite a lack of consensus on its application and evaluation.We sought to identify current frameworks guiding the evaluation AI for predictive analytics describe content these frameworks. We also assessed what stages along translational spectrum (ie, development, reporting, evaluation, implementation, surveillance) each framework has been discussed.We performed literature review regarding oversight medicine. The search included key topics such as "artificial intelligence," "machine learning," "guidance topic," "translational science," spanned time period 2014-2022. Documents were if they provided generalizable guidance use or Included are summarized descriptively subjected analysis. A novel matrix was developed applied appraise frameworks' coverage areas across stages.Fourteen featured review, including six that provide descriptive eight reporting checklists medical applications AI. Content analysis revealed five considerations related frameworks: transparency, reproducibility, ethics, effectiveness, engagement. All include discussions while only half discuss most likely report stage development least surveillance.Existing notably offer less input role engagement surveillance. Identifying optimizing strategies essential ensure can meaningfully benefit patients other end users.

Language: Английский

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Methods for Clinical Evaluation of Artificial Intelligence Algorithms for Medical Diagnosis

Radiology, Journal Year: 2022, Volume and Issue: 306(1), P. 20 - 31

Published: Nov. 8, 2022

Adequate clinical evaluation of artificial intelligence (AI) algorithms before adoption in practice is critical. Clinical aims to confirm acceptable AI performance through adequate external testing and the benefits AI-assisted care compared with conventional appropriately designed conducted studies, for which prospective studies are desirable. This article explains some fundamental methodological points that should be considered when designing appraising medical diagnosis. The specific topics addressed include following: (a) importance strategies conducting effectively, (b) various metrics graphical methods evaluating as well essential note using interpreting them, (c) paired study designs primarily comparative diagnoses, (d) parallel effect intervention an emphasis on randomized trials, (e) up-to-date guidelines reporting AI, registered EQUATOR Network library. Sound knowledge these will aid design, execution, reporting, appraisal AI.

Language: Английский

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Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review

International Journal of Medical Informatics, Journal Year: 2023, Volume and Issue: 173, P. 105026 - 105026

Published: Feb. 28, 2023

Wearable sensors have shown promise as a non-intrusive method for collecting biomarkers that may correlate with levels of elevated stress. Stressors cause variety biological responses, and these physiological reactions can be measured using including Heart Rate Variability (HRV), Electrodermal Activity (EDA) (HR) represent the stress response from Hypothalamic-Pituitary-Adrenal (HPA) axis, Autonomic Nervous System (ANS), immune system. While Cortisol magnitude remains gold standard indicator assessment [1], recent advances in wearable technologies resulted availability number consumer devices capable recording HRV, EDA HR sensor biomarkers, amongst other signals. At same time, researchers been applying machine learning techniques to recorded order build models able predict stress.The aim this review is provide an overview utilized prior research specific focus on model generalization when public datasets training data. We also shed light challenges opportunities learning-enabled monitoring detection face.This study reviewed published works contributing and/or designed detecting their associated methods. The electronic databases Google Scholar, Crossref, DOAJ PubMed were searched relevant articles total 33 identified included final analysis. synthesized into three categories publicly available datasets, applied those, future directions. For studies reviewed, we analysis approach results validation generalization. quality was conducted accordance IJMEDI checklist [2].A are labeled detection. These most commonly produced biomarker data Empatica E4 device, well-studied, medical-grade wrist-worn provides notable Most contain less than twenty-four hours data, varied experimental conditions labeling methodologies potentially limit ability generalize unseen In addition, discuss previous show shortcomings areas such protocols, lack statistical power, validity ability.Health tracking growing popularity, while existing still requires further study, area will continue improvements newer more substantial become available.

Language: Английский

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Machine learning predictive models for acute pancreatitis: A systematic review

International Journal of Medical Informatics, Journal Year: 2021, Volume and Issue: 157, P. 104641 - 104641

Published: Nov. 10, 2021

Acute pancreatitis (AP) is a common clinical pancreatic disease. Patients with different severity levels have outcomes. With the advantages of algorithms, machine learning (ML) has gradually emerged in field disease prediction, assisting doctors decision-making.A systematic review was conducted using PubMed, Web Science, Scopus, and Embase databases, following Preferred Reporting Items for Systematic Reviews Meta-Analyses guidelines. Publication time limited from inception to 29 May 2021. Studies that used ML establish predictive tools AP were eligible inclusion. Quality assessment included studies accordance IJMEDI checklist.In this review, 24 2,913 articles, total 8,327 patients 47 models, included. The could be divided into five categories: 10 (42%) reported prediction; (42%), complication 3 (13%), mortality 2 (8%), recurrence surgery timing prediction. showed great accuracy several prediction tasks. However, most retrospective nature, at single centre, based on database data, lacked external validation. According checklist our scoring criteria, two considered high quality. Most had an obvious bias quality data preparation, validation, deployment dimensions.In tasks AP, shown potential decision-making. existing still some deficiencies process model construction. Future need optimize further evaluate comparability systems performance, so as consequently develop high-quality ML-based models can practice.

Language: Английский

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Machine learning predicts cancer-associated deep vein thrombosis using clinically available variables

International Journal of Medical Informatics, Journal Year: 2022, Volume and Issue: 161, P. 104733 - 104733

Published: March 5, 2022

Language: Английский

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Predictive models for clinical decision making: Deep dives in practical machine learning

Journal of Internal Medicine, Journal Year: 2022, Volume and Issue: 292(2), P. 278 - 295

Published: April 15, 2022

The deployment of machine learning for tasks relevant to complementing standard care and advancing tools precision health has gained much attention in the clinical community, thus meriting further investigations into its broader use. In an introduction predictive modelling using learning, we conducted a review recent literature that explains taxonomies, terminology central concepts broad readership. Articles aimed at readers with little or no prior experience commonly used methods typical workflows were summarised key references are highlighted. Continual interdisciplinary developments data science, biostatistics epidemiology also motivated us discuss emerging topics data-driven (hypothesis-less) analytics learning. Through two methodological deep dives examples from psychiatry outcome prediction after lymphoma, highlight how use of, example, natural language processing can outperform established risk scores aid dynamic adaptive strategies. Such realistic detailed allow critical analysis importance new technological advances artificial intelligence decision-making. New decision support systems assist prevention by leveraging medicine.

Language: Английский

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Machine learning algorithms for early sepsis detection in the emergency department: A retrospective study

International Journal of Medical Informatics, Journal Year: 2022, Volume and Issue: 160, P. 104689 - 104689

Published: Jan. 20, 2022

Language: Английский

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