Public Disclosure of Results From Artificial Intelligence/Machine Learning Research in Health Care: Comprehensive Analysis of ClinicalTrials.gov, PubMed, and Scopus Data (2010-2023) (Preprint) DOI
Shoko Maru, Ryohei Kuwatsuru,

Michael Matthias

и другие.

Опубликована: Июль 30, 2024

BACKGROUND Despite the rapid growth of research in artificial intelligence/machine learning (AI/ML), little is known about how often study results are disclosed years after completion. OBJECTIVE We aimed to estimate proportion AI/ML that reported through ClinicalTrials.gov or peer-reviewed publications indexed PubMed Scopus. METHODS Using data from Clinical Trials Transformation Initiative Aggregate Analysis ClinicalTrials.gov, we identified studies initiated and completed between January 2010 December 2023 contained AI/ML-specific terms official title, brief summary, interventions, conditions, detailed descriptions, primary outcomes, keywords. For 842 studies, searched Scopus for containing identifiers relevant fields, such as abstract, calculated disclosure rates within 3 completion median times disclosure—from “primary date” “results first posted on earliest date journal publication. RESULTS Of (n=357 interventional; n=485 observational), 5.5% (46/842) 13.9% (117/842) publications, 17.7% (149/842) either route Higher were observed trials: 10.4% (37/357) 19.3% (69/357) 26.1% (93/357) route. Randomized controlled trials had even higher rates: 11.3% (23/203) 24.6% (50/203) 32% (65/203) Nevertheless, most findings (82.3%; 693/842) remained undisclosed using randomization (vs nonrandomized) masking open label) shorter disclosure. Most (85%; 305/357) sample sizes ≤1000, yet larger (n>1000) publication (30.8%; 16/52) than smaller (n≤1000) (17.4%; 53/305). Hospitals (12.4%; 42/340), academia (15.1%; 39/259), industry (13.7%; 20/146) published most. High-income countries accounted 82.4% (89/108) all studies. with results, report 505 days (IQR 399-676) 407 257-674), respectively. Open-label common (60%; 214/357). Single-center designs prevalent both (83.3%; 290/348) observational 377/458). CONCLUSIONS over 80% during 2010-2023, completion, raising questions representativeness publicly available evidence. While methodological rigor was generally associated rates, predominance single-center high-income may limit generalizability currently accessible.

Язык: Английский

The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century DOI Creative Commons
Shiva Maleki Varnosfaderani, Mohamad Forouzanfar

Bioengineering, Год журнала: 2024, Номер 11(4), С. 337 - 337

Опубликована: Март 29, 2024

As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging a key force transformation. This review motivated by urgent need to harness AI’s potential mitigate these issues aims critically assess integration in different domains. We explore how AI empowers clinical decision-making, optimizes hospital operation management, refines medical image analysis, revolutionizes patient care monitoring through AI-powered wearables. Through several case studies, we has transformed specific domains discuss remaining possible solutions. Additionally, will methodologies assessing solutions, ethical of deployment, importance data privacy bias mitigation responsible technology use. By presenting critical assessment transformative potential, this equips researchers with deeper understanding current future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, technologists navigate complexities implementation, fostering development AI-driven solutions that prioritize standards, equity, patient-centered approach.

Язык: Английский

Процитировано

218
Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review DOI Creative Commons
Yue Cai, Yuqing Cai, Liying Tang

и другие.

BMC Medicine, Год журнала: 2024, Номер 22(1)

Опубликована: Фев. 5, 2024

Abstract Background A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool AI models (AI-Ms) independent external validation are lacking. This systematic review aims to identify, describe, appraise AI-Ms CVD in the general special populations develop new score (IVS) replicability evaluation. Methods PubMed, Web Science, Embase, IEEE library were searched up July 2021. Data extraction analysis performed populations, distribution, predictors, algorithms, etc. The risk bias was evaluated with assessment (PROBAST). Subsequently, we designed IVS model evaluation five steps items, including transparency performance models, feasibility reproduction, clinical implication, respectively. is registered PROSPERO (No. CRD42021271789). Results In 20,887 screened references, 79 articles (82.5% 2017–2021) included, which contained 114 datasets (67 Europe North America, but 0 Africa). We identified 486 AI-Ms, majority development ( n = 380), none them had undergone validation. total 66 idiographic algorithms found; however, 36.4% used only once 39.4% over three times. large number different predictors (range 5–52,000, median 21) large-span sample size 80–3,660,000, 4466) observed. All at high according PROBAST, primarily due incorrect use statistical methods. confirmed 10 as “recommended”; 281 187 “not recommended” “warning,” Conclusion has led digital revolution field prediction, still early stage defects research design, report, systems. developed may contribute this field.

Язык: Английский

Процитировано

31
Self-reported checklists and quality scoring tools in radiomics: a meta-research DOI
Burak Koçak, Tugba Akinci D’Antonoli, Ece Ateş

и другие.

European Radiology, Год журнала: 2024, Номер 34(8), С. 5028 - 5040

Опубликована: Янв. 5, 2024

Язык: Английский

Процитировано

14
Development and Validation of a Deep Learning Algorithm for Differentiation of Choroidal Nevi from Small Melanoma in Fundus Photographs DOI Creative Commons
Shiva Sabazade, Marco A. Lumia Michalski, Jakub Bartoszek

и другие.

Ophthalmology Science, Год журнала: 2024, Номер 5(1), С. 100613 - 100613

Опубликована: Авг. 30, 2024

Язык: Английский

Процитировано

8
Meta-research on reporting guidelines for artificial intelligence: are authors and reviewers encouraged enough in radiology, nuclear medicine, and medical imaging journals? DOI Creative Commons
Burak Koçak, Ali Keleş, Fadime Köse

и другие.

Diagnostic and Interventional Radiology, Год журнала: 2024, Номер 0(0), С. 0 - 0

Опубликована: Фев. 20, 2024

To determine how radiology, nuclear medicine, and medical imaging journals encourage mandate the use of reporting guidelines for artificial intelligence (AI) in their author reviewer instructions. METHODSThe primary source journal information associated citation data used was Journal Citation Reports (June 2023 release 2022 data; Clarivate Analytics, UK).The first-and second-quartile indexed Science Index Expanded Emerging Sources were included.The instructions evaluated by two independent readers, followed an additional reader consensus, with assistance automatic annotation.Encouragement submission requirements systematically analyzed.The grouped as AI-specific, related to modeling, unrelated modeling. RESULTSOut 102 journals, 98 included this study, all them had instructions.Only five (5%) encouraged authors follow AI-specific guidelines.Among these, three required a filled-out checklist.Reviewer found 16 (16%), among which one (6%) reviewers without requirements.The proportions encouragement statistically significantly lower compared those other types (P < 0.05 all). CONCLUSIONThe findings indicate that are not commonly mandated (i.e., requiring checklist) these modeling leaving vast space improvement.This meta-research study hopes contribute awareness community AI ignite large-scale group efforts stakeholders, making research less wasteful. CLINICAL SIGNIFICANCEThis highlights need improved journals.This can potentially foster greater motivate various stakeholders collaborate promote more efficient responsible practices.

Язык: Английский

Процитировано

5
Examining the role of artificial intelligence to advance knowledge and address barriers to research in eating disorders DOI
Mark L. Norris, Nicole Obeid, Khaled El Emam

и другие.

International Journal of Eating Disorders, Год журнала: 2024, Номер 57(6), С. 1357 - 1368

Опубликована: Апрель 10, 2024

To provide a brief overview of artificial intelligence (AI) application within the field eating disorders (EDs) and propose focused solutions for research.

Язык: Английский

Процитировано

4
Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS) DOI Creative Commons
Khaled El Emam, Tiffany I. Leung, Bradley Malin

и другие.

Journal of Medical Internet Research, Год журнала: 2024, Номер 26, С. e52508 - e52508

Опубликована: Май 2, 2024

The number of papers presenting machine learning (ML) models that are being submitted to and published in the Journal Medical Internet Research other JMIR Publications journals has steadily increased. Editors peer reviewers involved review process for such manuscripts often go through multiple cycles enhance quality completeness reporting. use reporting guidelines or checklists can help ensure consistency (and published) scientific and, example, avoid instances missing information. In this Editorial, editors discuss general policy regarding authors’ application specifically focus on ML studies journals, using Consolidated Reporting Machine Learning Studies (CREMLS) guidelines, with an example how authors could CREMLS checklist transparency rigor

Язык: Английский

Процитировано

4
Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions (Preprint) DOI Creative Commons
Yuqing Cai, Da-Xin Gong,

Li-Ying Tang

и другие.

Journal of Medical Internet Research, Год журнала: 2024, Номер 26, С. e47645 - e47645

Опубликована: Июнь 13, 2024

In recent years, there has been explosive development in artificial intelligence (AI), which widely applied the health care field. As a typical AI technology, machine learning models have emerged with great potential predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, are expected to play crucial role reducing incidence mortality rates diseases. Although field become research hot spot, still many pitfalls that researchers need pay close attention to. These may affect predictive performance, credibility, reliability, reproducibility studied models, ultimately value affecting prospects clinical application. Therefore, identifying avoiding these is task before implementing research. However, currently lack comprehensive summary on this topic. This viewpoint aims analyze existing problems terms quality, set characteristics, model design, statistical methods, as well implications, provide possible solutions problems, such gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using specific algorithms address targeted issues, standardizing outcomes evaluation criteria, enhancing fairness replicability, goal offering reference assistance researchers, algorithm developers, policy makers, practitioners.

Язык: Английский

Процитировано

4
Noninvasive Oral Hyperspectral Imaging–Driven Digital Diagnosis of Heart Failure With Preserved Ejection Fraction: Model Development and Validation Study DOI Creative Commons
Xiaomeng Yang, Zeyan Li, Lei Lei

и другие.

Journal of Medical Internet Research, Год журнала: 2025, Номер 27, С. e67256 - e67256

Опубликована: Янв. 7, 2025

Background Oral microenvironmental disorders are associated with an increased risk of heart failure preserved ejection fraction (HFpEF). Hyperspectral imaging (HSI) technology enables the detection substances that visually indistinguishable to human eye, providing a noninvasive approach extensive applications in medical diagnostics. Objective The objective this study is develop and validate digital, oral diagnostic model for patients HFpEF using HSI combined various machine learning algorithms. Methods Between April 2023 August 2023, total 140 were recruited from Renmin Hospital Wuhan University serve as training internal testing groups study. Subsequently, 2024 September 2024, additional 35 enrolled Three Gorges Yichang Central People’s constitute external group. After preprocessing ensure image quality, spectral textural features extracted images. We 25 bands each patient obtained 8 corresponding texture evaluate performance 28 algorithms their ability distinguish control participants HFpEF. demonstrating optimal both was selected construct model. significant identifying identified further interpretative analysis. Shapley Additive Explanations (SHAP) used provide analytical insights into feature importance. Results Participants divided group (n=105), (n=35), consistent baseline characteristics across groups. Among tested, random forest algorithm demonstrated superior area under receiver operating characteristic curve (AUC) 0.884 accuracy 82.9% group, well AUC 0.812 85.7% For interpretation, we top by algorithm. SHAP analysis revealed discernible distinctions between HFpEF, thereby validating model’s capacity accurately identify Conclusions This efficient facilitates identification individuals promoting early detection, diagnosis, treatment. Our research presents clinically advanced framework validated independent data sets potential enhance care. Trial Registration China Clinical Registry ChiCTR2300078855; https://www.chictr.org.cn/showproj.html?proj=207133

Язык: Английский

Процитировано

0
EHR-based prediction modelling meets multimodal deep learning: A systematic review of structured and textual data fusion methods DOI
Ariel Soares Teles, Ivan Rodrigues de Moura, Francisco Silva

и другие.

Information Fusion, Год журнала: 2025, Номер unknown, С. 102981 - 102981

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0