Intelligence-based medicine最新文献

{"title":"Artificial intelligence in child development monitoring: A systematic review on usage, outcomes and acceptance","authors":"Lisa Reinhart ,&nbsp;Anne C. Bischops ,&nbsp;Janna-Lina Kerth ,&nbsp;Maurus Hagemeister ,&nbsp;Bert Heinrichs ,&nbsp;Simon B. Eickhoff ,&nbsp;Juergen Dukart ,&nbsp;Kerstin Konrad ,&nbsp;Ertan Mayatepek ,&nbsp;Thomas Meissner","doi":"10.1016/j.ibmed.2024.100134","DOIUrl":"10.1016/j.ibmed.2024.100134","url":null,"abstract":"<div><h3>Objectives</h3><p>Recent advances in Artificial Intelligence (AI) offer promising opportunities for its use in pediatric healthcare. This is especially true for early identification of developmental problems where timely intervention is essential, but developmental assessments are resource-intensive. AI carries potential as a valuable tool in the early detection of such developmental issues. In this systematic review, we aim to synthesize and evaluate the current literature on AI-usage in monitoring child development, including possible clinical outcomes, and acceptability of such technologies by different stakeholders.</p></div><div><h3>Material and methods</h3><p>The systematic review is based on a literature search comprising the databases PubMed, Cochrane Library, Scopus, Web of Science, Science Direct, PsycInfo, ACM and Google Scholar (time interval 1996–2022). All articles addressing AI-usage in monitoring child development or describing respective clinical outcomes and opinions were included.</p></div><div><h3>Results</h3><p>Out of 2814 identified articles, finally 71 were included. 70 reported on AI usage and one study dealt with users’ acceptance of AI. No article reported on potential clinical outcomes of AI applications. Articles showed a peak from 2020 to 2022. The majority of studies were from the US, China and India (n = 45) and mostly used pre-existing datasets such as electronic health records or speech and video recordings. The most used AI methods were support vector machines and deep learning.</p></div><div><h3>Conclusion</h3><p>A few well-proven AI applications in developmental monitoring exist. However, the majority has not been evaluated in clinical practice. The subdomains of cognitive, social and language development are particularly well-represented. Another focus is on early detection of autism. Potential clinical outcomes of AI usage and user's acceptance have rarely been considered yet. While the increase of publications in recent years suggests an increasing interest in AI implementation in child development monitoring, future research should focus on clinical practice application and stakeholder's needs.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"9 ","pages":"Article 100134"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000012/pdfft?md5=069d33a41736fe9c351d51eab8c166bf&pid=1-s2.0-S2666521224000012-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139877435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of deep learning to predict tinnitus patient outcomes","authors":"Katherine S. Adcock ,&nbsp;Gabriel Byczynski ,&nbsp;Emma Meade ,&nbsp;Sook Ling Leong ,&nbsp;Richard Gault ,&nbsp;Hubert Lim ,&nbsp;Sven Vanneste","doi":"10.1016/j.ibmed.2024.100141","DOIUrl":"https://doi.org/10.1016/j.ibmed.2024.100141","url":null,"abstract":"<div><p>Advances in machine and deep learning techniques provide a novel approach in understanding complex patterns within large datasets, leading to an implementation of personalized medicine approaches to support clinical decision making. Results from recent clinical trials (TENT-A1 and TENT-A2 studies; clinicaltrials.gov: <span>NCT02669069</span><svg><path></path></svg> and <span>NCT03530306</span><svg><path></path></svg>) support that a novel bimodal neuromodulation approach could be a breakthrough treatment for patients with tinnitus, which adversely affects 10–15 % of the population. Given the heterogeneity of symptoms, it is important to identify whether treatment has an optimal effect on specific subgroups of tinnitus patients. The current study is a first look at the feasibility of using deep learning modelling on patient reported data to predict treatment outcomes in individuals with tinnitus, and highlights what features are most beneficial for clinical decision making.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"9 ","pages":"Article 100141"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000085/pdfft?md5=be723d4e20025718809aab06a9a42aa7&pid=1-s2.0-S2666521224000085-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141097355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of cardiovascular disease using explainable artificial intelligence and gut microbiota data","authors":"Can Duyar ,&nbsp;Simone Oliver Senica ,&nbsp;Habil Kalkan","doi":"10.1016/j.ibmed.2024.100180","DOIUrl":"10.1016/j.ibmed.2024.100180","url":null,"abstract":"<div><h3>Purpose:</h3><div>Gut microbiota are defined as the microbial population of the intestines. They include various types of bacteria which can influence and predict the existence or onset of some specific diseases. Therefore, it is a common practice in medicine to analyze the gut microbiota for diagnostic purposes by analyzing certain measurable biochemical features associated with the disease under investigation. However, the evaluation of all the data collected from the gut microbiota is a labor-intensive process. Artificial Intelligence (AI) may be a helpful tool to identify the hidden patterns in gut microbiota for the detection of disease and other classification problems.</div></div><div><h3>Methods:</h3><div>In this study, we propose a deep neural model based on a one-dimensional convolutional neural network (1D-CNN) to detect cardiovascular disease using bacterial taxonomy and OTU (Operational Taxonomic Unit) table data. The developed AI method is compared to classical machine learning algorithms, regression, boosting algorithms and a deep model, Tabular Network (TabNet), developed for tabular data and obtained outperforming classification results.</div></div><div><h3>Results:</h3><div>According to AUC (Area Under Curve) values, boosting and regression methods outperformed the classical machine learning methods. However, the highest value of 97.09 AUC was obtained with the developed 1D-CNN model by using bacterial taxonomy data even with less then expected number of samples. Using explainable AI, nine bacteria were identified which the models find important for classification.</div></div><div><h3>Conclusion:</h3><div>The proposed method is robust and well adapted to taxonomy data in tabular form. It can be easily adapted to detect other diseases by using taxonomy data. The study also investigated the effect on barcode sequence for the classification, but the result showed that barcode sequences do not contribute to the bacterial taxonomy data for the estimation of CVD disease.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100180"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of contactless human vital signs monitoring device with remote-photoplethysmography using adaptive region-of-interest and hybrid processing methods","authors":"Dessy Novita ,&nbsp;Fajar Wira Adikusuma ,&nbsp;Nanang Rohadi ,&nbsp;Bambang Mukti Wibawa ,&nbsp;Agus Trisanto ,&nbsp;Irma Ruslina Defi ,&nbsp;Sherllina Rizqi Fauziah","doi":"10.1016/j.ibmed.2024.100160","DOIUrl":"10.1016/j.ibmed.2024.100160","url":null,"abstract":"<div><p>Vital sign assessment is an examination that indicates changes in health. Direct contact during vital signs assessment can increase the risk of disease transmission. This research aimed to develop a contactless vital sign monitoring prototype that includes heart rate, respiratory rate, blood pressure, and oxygen saturation using a digital camera based on remote photoplethysmography with an adaptive region of interest. The adaptive region-of-interest method uses face detection and skin segmentation to generate red-green-blue signals, taking only the skin pixels of the patients while also minimising the effect of motion artefacts. The hybrid processing method combines several vital sign extraction methods to filter external irrelevant factors and produce heart rate, respiratory rate, blood pressure, and blood oxygen saturation values. In addition, the prototype was tested on 50 participants using standard vital sign assessment tools for comparison. The technical specification test of the prototype concluded that the optimal distance of this prototype was up to 2 m with a processing time of 2 s for every 1-s video. The vital signs results were presented using Bland-Altman, which showed that although the Bland-Altman plots revealed a substantial variance in the limits of agreement (±15–20 mmHg for blood pressure, ±15–17 bpm for heart rate, ±4–6 bpm for respiratory rate, and ±1–3 % for blood oxygen saturation), the mean differences for all vital signs were small (±0.7–5 mmHg for blood pressure, ±0.4–0.6 bpm for heart rate, ±0.5–0.7 bpm for respiratory rate, ±0.4–0.6 for blood oxygen saturation) and most data points were within the limits. While further clinical studies are needed to assess its reliability in monitoring specific medical conditions, the prototype has shown an acceptable agreement in assessing vital signs compared to the conventional methods, making it feasible for further development into a medical device.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100160"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000279/pdfft?md5=9c2a08467d4ad925fd1a09dfb6f59ae1&pid=1-s2.0-S2666521224000279-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141843363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating the prevalence of diabetic retinopathy in electronic health records with massive missing labels","authors":"Ye Liang ,&nbsp;Ru Wang ,&nbsp;Yuchen Wang ,&nbsp;Tieming Liu","doi":"10.1016/j.ibmed.2024.100154","DOIUrl":"https://doi.org/10.1016/j.ibmed.2024.100154","url":null,"abstract":"<div><h3>Objective</h3><p>The paper aims to address the problem of massive unlabeled patients in electronic health records (EHR) who potentially have undiagnosed diabetic retinopathy (DR). It is desired to estimate the actual DR prevalence in EHR with 96 % missing labels.</p></div><div><h3>Materials and methods</h3><p>The Cerner Health Facts data are used in the study, with 3749 labeled DR patients and 97,876 unlabeled diabetic patients. This extensive dataset spans the demographics of the United States over the past two decades. We implemented state-of-art positive-unlabeled learning methods, including ensemble-based support vector machine, ensemble-based random forest, and Bayesian finite mixture modeling.</p></div><div><h3>Results</h3><p>The estimated DR prevalence in the population represented by Cerner EHR is approximately 25 % and the classification techniques generally achieve an AUC of around 87 %. As a by-product, a predictive inference on the risk of DR based on a patient's personalized medical information is derived.</p></div><div><h3>Discussion</h3><p>Missing labels is a common issue for EHR data quality. Ignoring these missing labels can lead to biased results in the analyses of EHR data. The problem is especially severe in the context of DR. It is thus important to use machine learning or statistical tools to identify the unlabeled patients. The tool in this paper helps both data analysts and clinicians in their practices.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100154"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000218/pdfft?md5=0b269311073371904a3317a4df15d0e5&pid=1-s2.0-S2666521224000218-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141606989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive modeling of Alzheimer's disease progression: Integrating temporal clinical factors and outcomes in time series forecasting","authors":"K.H. Aqil ,&nbsp;Prashanth Dumpuri ,&nbsp;Keerthi Ram ,&nbsp;Mohanasankar Sivaprakasam","doi":"10.1016/j.ibmed.2024.100159","DOIUrl":"10.1016/j.ibmed.2024.100159","url":null,"abstract":"<div><p>Alzheimer's disease is a complex neurodegenerative disorder that profoundly impacts millions of individuals worldwide, presenting significant challenges in both diagnosis and treatment. Recent advances in deep learning-based methods have shown promising potential for predicting disease progression using multimodal data. However, the majority of studies in this domain have predominantly focused on cross-sectional data, neglecting the crucial temporal dimension of the disease's progression. In this study, we propose a novel approach to predict the progression of Alzheimer's disease by leveraging a multimodal time-series forecasting system based on graph representation learning. Our approach incorporates a Temporal Graph Network encoder, employing k-nearest neighbors and Cumulative Bayesian Ridge with high correlation imputation to generate graph node embeddings at each time step. Furthermore, we employ an Encoder-Decoder architecture, where a Graph Attention Network translates a dynamic graph into node embeddings, and a decoder estimates future edge probabilities. When utilizing all available patient features in the ADNI dataset, our proposed method achieved an Area Under the Curve (AUC) of 0.8090 for dynamic edge prediction. Furthermore, for neuroimaging data, the AUC improved significantly to 0.8807.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100159"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521224000267/pdfft?md5=966a05e54125ad7b71aab383d1ad9557&pid=1-s2.0-S2666521224000267-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141736590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic characterization of cerebral MRI images for the detection of autism spectrum disorders","authors":"Nour El Houda Mezrioui ,&nbsp;Kamel Aloui ,&nbsp;Amine Nait-Ali ,&nbsp;Mohamed Saber Naceur","doi":"10.1016/j.ibmed.2023.100127","DOIUrl":"https://doi.org/10.1016/j.ibmed.2023.100127","url":null,"abstract":"<div><p>Autism Spectrum Disorders (ASD) are one of the most serious health problems that our generation is facing [1]. It affects around one out of every 54 children and causes issues with social interaction, communication [2] and repetitive behaviors [3]. The development of full biomarkers for neuroimaging is a crucial step in diagnosing and tailoring medical care for autism spectrum disorder [4]. Volumetric studies focused on 3D MRI texture features have shown a high capacity for detecting abnormalities and characterizing variations caused by tissue heterogeneity. Recently, it has been the interest of comprehensive studies. However, only a few studies have aimed to investigate the link between object texture and ASD. This paper suggests a framework based on geometric texture features analyzing the variations between ASD and development control (DC) subjects. Our study uses 1114 T1-weighted MRI scans from two groups of subjects: 521 individuals with ASD and 593 controls (age range: 6–64 years) [5], divided into three broad age groups. We then computed the features from automatically labeled subcortical and cortical regions and encoded them as texture features by applying seven global Riemannian geometry descriptors and eight local features of standard Harlicks quantifier functions. Significant tests were used to identify texture volumetric differences between ASD and DC subjects. The most discriminative features are selected by applying the Correlation Matrix, and these features are used to classify the two classes using an Artificial Neural Network analysis. Preliminary results indicate that in ASD subjects, all 15 structure-derived features and subcortical regions tested have significantly different distributions from DC subjects.</p></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"9 ","pages":"Article 100127"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666521223000418/pdfft?md5=52f7350c7f1b4866d790132947d0352d&pid=1-s2.0-S2666521223000418-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139737405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic literature review and meta-analysis for real-world versus clinical validation performance of artificial intelligence applications indicated for ICH and LVO detection","authors":"Jason Le ,&nbsp;Oisín Butler ,&nbsp;Ann-Kathrin Frenz ,&nbsp;Ankur Sharma","doi":"10.1016/j.ibmed.2024.100187","DOIUrl":"10.1016/j.ibmed.2024.100187","url":null,"abstract":"<div><h3>Purpose</h3><div>We sought to compare the performance of AI applications in real-world studies to validation study data used to gain regulatory approval.</div></div><div><h3>Methods</h3><div>We searched PubMed, EBSCO, and EMBASE for publications from 2018 to 2023. We included articles that evaluated the sensitivity and specificity of ICH and LVO detection applications in real-world populations. We performed a quality and applicability assessment using QUADAS-2. We used a bivariate or two univariate meta-analyses, where appropriate, to calculate summary point estimates for sensitivity and specificity.</div></div><div><h3>Results</h3><div>Eighteen articles met the criteria of the systematic literature review. The included articles evaluated five applications indicated for ICH or LVO triage. Three of the five applications yielded adequate studies to be included in the meta-analysis. For most applications, we did not observe any systematic differences in sensitivity and specificity results between the point estimates from the meta-analysis and the respective 510k studies. For VIZ LVO and RAPID LVO, the 95 % CI for real-world sensitivity sat within the 95 % CI from their respective validation study. For BriefCase ICH, the 95 % CI for real-world sensitivity sat below the 95 % CI of the respective validation study. Additionally, the 95 % CI for real-world specificity for all three of the applications sat within the 95 % CI of their respective validation studies. Data from the individual real-world studies for RAPID ICH and CINA LVO followed a similar trend.</div></div><div><h3>Conclusion</h3><div>The performance of applications in real-world settings was non-inferior to the performance observed in validation studies used to obtain 510k clearance.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100187"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NUC-Fuse: Multimodal medical image fusion using nuclear norm & classification of brain tumors using ARBFN","authors":"Shihabudeen H. ,&nbsp;Rajeesh J.","doi":"10.1016/j.ibmed.2024.100181","DOIUrl":"10.1016/j.ibmed.2024.100181","url":null,"abstract":"<div><div>Medical imaging has been widely used to diagnose diseases over the past two decades. The lack of information in this field makes it difficult for medical experts to diagnose diseases with a single modality. The combination of image fusion techniques enables the integration of pictures depicting various tissues and disorders from multiple medical imaging devices, facilitating enhanced research and treatment by providing complementary information through multimodal medical imaging fusion. The proposed work employs the nuclear norm and residual connections to combine the complementary features from both CT and MRI imaging approaches. The autoencoder eventually creates a merged image. The fused pictures are categorized as benign or malignant in the following phase using the present Radial Basis Function Network (RBFN). The performance measures, such as Mutual Information, Structural Similarity Index Measure, <span><math><msub><mrow><mi>Q</mi></mrow><mrow><mi>w</mi></mrow></msub></math></span>, and <span><math><msub><mrow><mi>Q</mi></mrow><mrow><mi>e</mi></mrow></msub></math></span>, have shown improved values, specifically 4.6328, 0.6492, 0.8300, and 0.8185 respectively, when compared with different fusion methods. Additionally, the classification algorithm yields 97% accuracy, 89% precision, and 92% recall when combined with the proposed fusion algorithm.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100181"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The trend of artificial intelligence application in medicine and neurology; the state-of-the-art systematic scoping review 2010–2022","authors":"Mohammad Hossein Abbasi ,&nbsp;Melek Somai ,&nbsp;Hamidreza Saber","doi":"10.1016/j.ibmed.2024.100179","DOIUrl":"10.1016/j.ibmed.2024.100179","url":null,"abstract":"<div><h3>Background</h3><div>Artificial Intelligence (AI) is an increasingly popular research focus for multiple areas of science. The trend of using AI-based clinical research in different fields of medicine and defining the shortcomings of those trials will guide researchers and future studies.</div></div><div><h3>Methods</h3><div>We systematically reviewed trials registered in <span><span>ClinicalTrials.gov</span><svg><path></path></svg></span> that apply AI in clinical research. We explored the trend of AI-applied clinical research and described the design and conduct of such trials. Also, we considered high-quality trials to represent their enrollees’ and other characteristics.</div></div><div><h3>Results</h3><div>Our search yielded 839 trials involving a direct application of AI, among which 330 (39.3 %) trials were interventional, and the rest were observational (60.7 %). Most of the studies aimed to improve diagnosis (70.2 %); in less than a quarter of trials, management was targeted (22.8 %), and AI was implemented in an acute setting (13 %). Gastrointestinal, cardiovascular, and neurology were the significant fields of medicine with the application of AI in their research. High-quality published AI trials showed good generalizability in terms of their enrollees’ characteristics, with an average age of 52.46 years old and 50.28 % female participants.</div></div><div><h3>Conclusion</h3><div>The incorporation of AI in different fields of medicine needs to be more balanced, and attempts should be made to broaden the spectrum of AI-based clinical research and to improve its deployment in real-world practice.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"10 ","pages":"Article 100179"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}