{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T20:55:20Z","timestamp":1790974520388,"version":"4.1.0"},"reference-count":65,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:00:00Z","timestamp":1778803200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006418","name":"Yarmouk University","doi-asserted-by":"publisher","award":["08\/2024"],"award-info":[{"award-number":["08\/2024"]}],"id":[{"id":"10.13039\/501100006418","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>cardiovascular disease is the leading global cause of mortality. Photoplethysmography (PPG), widely embedded in consumer wearables, offers a scalable diagnostic modality. However, prior approaches are often constrained by handcrafted features, binary classification, and poor generalizability, limiting their clinical impact.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A deep hierarchical convolutional neural network (CNN) was designed to extract both morphological and rhythmic characteristics directly from raw photoplethysmography (PPG) signals. The architecture employs progressively structured convolutional filter hierarchies to capture multi-scale signal features. To enhance signal stability and training efficiency, a dual-stage normalization strategy was implemented, consisting of Z-score standardization followed by Min\u2013Max scaling. In addition, batch normalization and dropout regularization were incorporated to improve model generalization and reduce the risk of overfitting. The proposed framework was trained and evaluated on a multi-source dataset comprising 612 patients and 2,448 annotated PPG segments distributed across six diagnostic classes: atrial fibrillation (AF), heart failure (HF), acute coronary syndrome (ACS), cerebral vascular accident (CVA), deep vein thrombosis (DVT), and normal sinus rhythm (NSR).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model achieved an overall accuracy of 93.48%, a macro-average F1-score of 0.9386, and a Cohen\u2019s Kappa of 0.8968, indicating \u201calmost perfect\u201d agreement. AF and HF were detected with flawless precision and recall (1.000), while ACS achieved high sensitivity (recall 0.964). Errors were primarily confined to physiologically related conditions (e.g., ACS vs. CVA). Inference efficiency was demonstrated with &lt;5\u2009ms per segment on consumer-grade hardware, confirming feasibility for real-time applications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed framework advances beyond lightweight but underpowered or overly complex models by combining representational depth with computational efficiency. Limitations include the need for external, multi-center validation and explainability integration. This study establishes a robust foundation for PPG-based, multi-class cardiovascular diagnostics, supporting clinical decision support and next-generation wearable health technologies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0347840","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T17:38:26Z","timestamp":1778866706000},"page":"e0347840","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi class photoplethysmography-based deep model for cardiovascular disease classification"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5190-3048","authenticated-orcid":true,"given":"Amjed","family":"Al Fahoum","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,5,15]]},"reference":[{"key":"pone.0347840.ref001","volume-title":"Cardiovascular diseases: fact sheet","author":"World Health Organization","year":"2023"},{"key":"pone.0347840.ref002","doi-asserted-by":"crossref","first-page":"114993","DOI":"10.1016\/j.sna.2023.114993","article-title":"A review on flexible wearables \u2013 Recent developments in non-invasive continuous health monitoring","volume":"366","author":"NL Kazanskiy","year":"2024","journal-title":"Sensors and Actuators A: Physical"},{"issue":"2","key":"pone.0347840.ref003","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6579\/ada9c1","article-title":"Generative adversarial networks with fully connected layers to denoise PPG signals","volume":"13","author":"IA Avila Castro","year":"2025","journal-title":"Physiol Meas"},{"issue":"1","key":"pone.0347840.ref004","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1504\/IJBET.2015.066962","article-title":"A multiple signal classification approach for photoplethysmography signals in healthy and athletic subjects","volume":"17","author":"ASA Fahoum","year":"2015","journal-title":"IJBET"},{"issue":"2","key":"pone.0347840.ref005","doi-asserted-by":"crossref","first-page":"249","DOI":"10.3390\/bioengineering10020249","article-title":"Identification of coronary artery diseases using photoplethysmography signals and practical feature selection process","volume":"10","author":"AS Al Fahoum","year":"2023","journal-title":"Bioengineering (Basel)"},{"issue":"21","key":"pone.0347840.ref006","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e39745","article-title":"Development of a novel light-sensitive PPG model using PPG scalograms and PPG-NET learning for non-invasive hypertension monitoring","volume":"10","author":"AA Fahoum","year":"2024","journal-title":"Heliyon"},{"key":"pone.0347840.ref007","first-page":"1","article-title":"PPG signal-based classification of blood pressure stages using wavelet transformation and pre-trained deep learning models","volume":"50","author":"A Al-Fahoum","year":"2023","journal-title":"Computing in Cardiology"},{"key":"pone.0347840.ref008","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/JIBEC63210.2024.10932024","article-title":"Comparative Analysis of PPG Signal Capturing Using Piezoelectric, Photosensor, and Earlobe Devices","author":"A Al-Fahoum","year":"2024","journal-title":"2024 Second Jordanian International Biomedical Engineering Conference (JIBEC)"},{"issue":"3","key":"pone.0347840.ref009","doi-asserted-by":"crossref","DOI":"10.1088\/0967-3334\/28\/3\/R01","article-title":"Photoplethysmography and its application in clinical physiological measurement","volume":"28","author":"J Allen","year":"2007","journal-title":"Physiol Meas"},{"key":"pone.0347840.ref010","doi-asserted-by":"crossref","first-page":"105689","DOI":"10.1016\/j.bspc.2023.105689","article-title":"Machine learning framework for Inter-Beat Interval estimation using wearable Photoplethysmography sensors","volume":"88","author":"VBO Fioravanti","year":"2024","journal-title":"Biomedical Signal Processing and Control"},{"key":"pone.0347840.ref011","doi-asserted-by":"crossref","first-page":"107336","DOI":"10.1016\/j.bspc.2024.107336","article-title":"Improving the quality of pulse rate variability derived from wearable devices using adaptive, spectrum and nonlinear filtering","volume":"102","author":"MA Prucnal","year":"2025","journal-title":"Biomedical Signal Processing and Control"},{"key":"pone.0347840.ref012","doi-asserted-by":"crossref","first-page":"116546","DOI":"10.1016\/j.measurement.2024.116546","article-title":"A cascaded approach of group sparse mode decomposition and deep neural network for heart rate estimation using reference signal-less PPG signal","volume":"246","author":"P Maan","year":"2025","journal-title":"Measurement"},{"issue":"15","key":"pone.0347840.ref013","doi-asserted-by":"crossref","first-page":"3420","DOI":"10.3390\/s19153420","article-title":"Blood pressure estimation from photoplethysmogram using a spectro-temporal deep neural network","volume":"19","author":"G Slapni\u010dar","year":"2019","journal-title":"Sensors (Basel)"},{"key":"pone.0347840.ref014","doi-asserted-by":"crossref","first-page":"101870","DOI":"10.1016\/j.bspc.2020.101870","article-title":"A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure","volume":"58","author":"C El-Hajj","year":"2020","journal-title":"Biomedical Signal Processing and Control"},{"issue":"14","key":"pone.0347840.ref015","doi-asserted-by":"crossref","first-page":"3079","DOI":"10.3390\/s19143079","article-title":"Deep PPG: Large-scale heart rate estimation with convolutional neural networks","volume":"19","author":"A Reiss","year":"2019","journal-title":"Sensors (Basel)"},{"key":"pone.0347840.ref016","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1038\/s41746-019-0136-7","article-title":"The use of photoplethysmography for assessing hypertension","volume":"2","author":"M Elgendi","year":"2019","journal-title":"NPJ Digit Med"},{"issue":"11","key":"pone.0347840.ref017","doi-asserted-by":"crossref","first-page":"3134","DOI":"10.1109\/TBME.2020.2976989","article-title":"PPG sensor contact pressure should be taken into account for cuff-less blood pressure measurement","volume":"67","author":"A Chandrasekhar","year":"2020","journal-title":"IEEE Trans Biomed Eng"},{"key":"pone.0347840.ref018","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1038\/s41746-019-0207-9","article-title":"Photoplethysmography based atrial fibrillation detection: a review","volume":"3","author":"T Pereira","year":"2020","journal-title":"NPJ Digit Med"},{"issue":"1","key":"pone.0347840.ref019","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s12938-021-00848-w","article-title":"Attention-based multi-scale features fusion for unobtrusive atrial fibrillation detection using ballistocardiogram signal","volume":"20","author":"F Jiang","year":"2021","journal-title":"Biomed Eng Online"},{"key":"pone.0347840.ref020","doi-asserted-by":"crossref","first-page":"107233","DOI":"10.1016\/j.bspc.2024.107233","article-title":"A 1D generative adversarial framework for atrial fibrillation detection using restored wrist PPG","volume":"101","author":"FR Sayem","year":"2025","journal-title":"Biomedical Signal Processing and Control"},{"issue":"23","key":"pone.0347840.ref021","doi-asserted-by":"crossref","first-page":"38767","DOI":"10.1109\/JIOT.2024.3454691","article-title":"Precision biometrics based on PPG measured from an IoT device with OPDs, real-time quality check through PSD, DC drift, and deep learning","volume":"11","author":"DT Ngo","year":"2024","journal-title":"IEEE Internet Things J"},{"key":"pone.0347840.ref022","first-page":"11","article-title":"Photoplethysmography-based biometric system: Evaluating deep learning techniques","author":"A Cherry","year":"2025","journal-title":"Applications in Electronics Pervading Industry, Environment and Society (APPLEPIES 2024)"},{"key":"pone.0347840.ref023","first-page":"1","article-title":"A deep-learning method for obstructive sleep apnea detection from single-channel PPG","author":"P Agrawal","year":"2025","journal-title":"Proc. IEEE ISCAS 2025"},{"issue":"1","key":"pone.0347840.ref024","doi-asserted-by":"crossref","DOI":"10.1186\/s43168-025-00438-7","article-title":"Arterial stiffness in former elderly smokers is associated with aging and smoking but not airflow limitation: a cross-sectional study","volume":"19","author":"S Kawachi","year":"2025","journal-title":"Egypt J Bronchol"},{"issue":"12","key":"pone.0347840.ref025","doi-asserted-by":"crossref","DOI":"10.1016\/j.hrthm.2025.07.060","article-title":"PPG-based smartphone application vs usual care for atrial fibrillation screening: A European multicenter randomized trial","volume":"22","author":"JJ Reichl","year":"2025","journal-title":"Heart Rhythm"},{"issue":"1","key":"pone.0347840.ref026","article-title":"Early detection of cardiovascular disease using photoplethysmography (PPG) signal analysis","volume":"2022","author":"P Y","year":"2024","journal-title":"intij"},{"issue":"6","key":"pone.0347840.ref027","doi-asserted-by":"crossref","first-page":"678","DOI":"10.3390\/bioengineering10060678","article-title":"Machine learning techniques for enhancing multi-classifier performance in CVD detection from PPG signals","volume":"10","author":"S Palanisamy","year":"2023","journal-title":"Bioengineering"},{"issue":"18","key":"pone.0347840.ref028","doi-asserted-by":"crossref","first-page":"2231","DOI":"10.1016\/j.jacc.2018.08.1038","article-title":"Fourth universal definition of myocardial infarction (2018)","volume":"72","author":"K Thygesen","year":"2018","journal-title":"J Am Coll Cardiol"},{"key":"pone.0347840.ref029","article-title":"Motion artifact removal techniques for wearable EEG and PPG sensor systems","volume":"2","author":"D Seok","year":"2021","journal-title":"FrontElectron"},{"key":"pone.0347840.ref030","article-title":"A review of deep learning methods for photoplethysmography data","author":"G Nie","year":"2024"},{"issue":"4","key":"pone.0347840.ref031","doi-asserted-by":"crossref","first-page":"2439","DOI":"10.1109\/JBHI.2024.3499834","article-title":"Wearable PPG Based BP Estimation Methods: A systematic review and meta-analysis","volume":"29","author":"Z Sastimoglu","year":"2025","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"pone.0347840.ref032","doi-asserted-by":"crossref","first-page":"15054","DOI":"10.1038\/s41598-019-49092-2","article-title":"Atrial fibrillation detection from wrist photoplethysmography signals using smartwatches","volume":"9","author":"SK Bashar","year":"2019","journal-title":"Sci Rep"},{"key":"pone.0347840.ref033","doi-asserted-by":"crossref","first-page":"103240","DOI":"10.1016\/j.sysarc.2024.103240","article-title":"Real-time intelligent on-device monitoring of heart rate variability with PPG sensors","volume":"154","author":"J Xu","year":"2024","journal-title":"Journal of Systems Architecture"},{"key":"pone.0347840.ref034","doi-asserted-by":"crossref","first-page":"108325","DOI":"10.1016\/j.engappai.2024.108325","article-title":"Exploring the power of photoplethysmogram matrix for atrial fibrillation detection with integrated explainability","volume":"133","author":"C Fuster-Barcel\u00f3","year":"2024","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"4","key":"pone.0347840.ref035","doi-asserted-by":"crossref","DOI":"10.1093\/europace\/euae065","article-title":"Real-world validation of smartphone-based photoplethysmography for rate and rhythm monitoring in atrial fibrillation","volume":"26","author":"H Gruwez","year":"2024","journal-title":"Europace"},{"issue":"4","key":"pone.0347840.ref036","first-page":"195","article-title":"A review on wearable photoplethysmography sensors and their potential future applications in health care","volume":"4","author":"D Castaneda","year":"2018","journal-title":"Int J Biosens Bioelectron"},{"issue":"5","key":"pone.0347840.ref037","doi-asserted-by":"crossref","first-page":"2063","DOI":"10.1109\/JBHI.2021.3131984","article-title":"Atrial fibrillation detection and atrial fibrillation burden estimation via wearables","volume":"26","author":"L Zhu","year":"2022","journal-title":"IEEE J Biomed Health Inform"},{"issue":"4","key":"pone.0347840.ref038","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6579\/ad37ee","article-title":"Photoplethysmography based atrial fibrillation detection: A continually growing field","volume":"45","author":"C Ding","year":"2024","journal-title":"Physiol Meas"},{"key":"pone.0347840.ref039","volume-title":"Photoplethysmography: technology, signal analysis and applications","author":"PA Kyriacou","year":"2021"},{"issue":"2","key":"pone.0347840.ref040","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/3578556","article-title":"Physiowise: A physics-aware approach to dicrotic notch identification","volume":"4","author":"M Saffarpour","year":"2023","journal-title":"ACM Trans Comput Healthc"},{"key":"pone.0347840.ref041","author":"C Gallego V\u00e1zquez","year":"2025"},{"issue":"1","key":"pone.0347840.ref042","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/TBME.2021.3087105","article-title":"Photoplethysmography fast upstroke time intervals can be useful features for cuff-less measurement of blood pressure changes in humans","volume":"69","author":"K Natarajan","year":"2022","journal-title":"IEEE Trans Biomed Eng"},{"issue":"1","key":"pone.0347840.ref043","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1038\/s44328-024-00002-1","article-title":"Optimal signal quality index for remote photoplethysmogram sensing","volume":"1","author":"M Elgendi","year":"2024","journal-title":"npj Biosensing"},{"key":"pone.0347840.ref044","doi-asserted-by":"crossref","first-page":"101007","DOI":"10.1016\/j.iot.2023.101007","article-title":"Tiny-PPG: A lightweight deep neural network for real-time detection of motion artifacts in photoplethysmogram signals on edge devices","volume":"25","author":"Y Zheng","year":"2024","journal-title":"Internet of Things"},{"issue":"7","key":"pone.0347840.ref045","article-title":"Multiclass arrhythmia detection and classification from photoplethysmography signals using a deep convolutional neural network","volume":"11","author":"Z Liu","year":"2022","journal-title":"J Am Heart Assoc"},{"issue":"3","key":"pone.0347840.ref046","doi-asserted-by":"crossref","first-page":"665","DOI":"10.3390\/electronics13030665","article-title":"Res-BiANet: A hybrid deep learning model for arrhythmia detection based on PPG signal","volume":"13","author":"Y Wu","year":"2024","journal-title":"Electronics"},{"issue":"2","key":"pone.0347840.ref047","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0314154","article-title":"Deep CNN-based detection of cardiac rhythm disorders using PPG signals from wearable devices","volume":"20","author":"MG Bulut","year":"2025","journal-title":"PLoS One"},{"issue":"8","key":"pone.0347840.ref048","doi-asserted-by":"crossref","first-page":"12863","DOI":"10.1109\/JSEN.2024.3371462","article-title":"Revisiting large-kernel cnn design via structural re-parameterization for sensor-based human activity recognition","volume":"24","author":"M Yao","year":"2024","journal-title":"IEEE Sensors Journal"},{"key":"pone.0347840.ref049","first-page":"1","article-title":"A CNN and transformer hybrid network for multi-class arrhythmia detection from photoplethysmography","volume":"2024","author":"Z-D Liu","year":"2024","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"pone.0347840.ref050","doi-asserted-by":"crossref","first-page":"2478","DOI":"10.23919\/EUSIPCO.2017.8081656","article-title":"Computationally efficient heart rate estimation during physical exercise using photoplethysmographic signals","author":"T Schack","year":"2017","journal-title":"2017 25th European Signal Processing Conference (EUSIPCO)"},{"issue":"17","key":"pone.0347840.ref051","doi-asserted-by":"crossref","first-page":"35875","DOI":"10.1109\/JIOT.2025.3579524","article-title":"A Dual-focus cloud\u2013edge collaborative framework in multitask hemodynamic parameter cross-scale analysis: The equilibrium of clinical performance and efficiency","volume":"12","author":"J Liu","year":"2025","journal-title":"IEEE Internet Things J"},{"key":"pone.0347840.ref052","doi-asserted-by":"crossref","first-page":"122388","DOI":"10.1016\/j.eswa.2023.122388","article-title":"ExHyptNet: An explainable diagnosis of hypertension using EfficientNet with PPG signals","volume":"239","author":"E-SA El-Dahshan","year":"2024","journal-title":"Expert Systems with Applications"},{"issue":"7","key":"pone.0347840.ref053","doi-asserted-by":"crossref","first-page":"2248","DOI":"10.3390\/s24072248","article-title":"Non-invasive heart failure evaluation using machine learning algorithms","volume":"24","author":"OA Victor","year":"2024","journal-title":"Sensors (Basel)"},{"key":"pone.0347840.ref054","doi-asserted-by":"crossref","first-page":"105479","DOI":"10.1016\/j.compbiomed.2022.105479","article-title":"A machine learning approach for hypertension detection based on photoplethysmography and clinical data","volume":"145","author":"E Martinez-R\u00edos","year":"2022","journal-title":"Comput Biol Med"},{"key":"pone.0347840.ref055","article-title":"Multiclass arrhythmia classification using multimodal smartwatch photoplethysmography signals collected in real-life settings","author":"D Han","year":"2024","journal-title":"Research Square"},{"issue":"4","key":"pone.0347840.ref056","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1007\/s13534-025-00473-9","article-title":"Advances in cardiovascular signal analysis with future directions: A review of machine learning and deep learning models for cardiovascular disease classification based on ECG, PCG, and PPG signals","volume":"15","author":"YN Fuadah","year":"2025","journal-title":"Biomed Eng Lett"},{"key":"pone.0347840.ref057","doi-asserted-by":"crossref","first-page":"104164","DOI":"10.1016\/j.bspc.2022.104164","article-title":"Machine learning-based medical decision support system for diagnosing HFpEF and HFrEF using PPG","volume":"79","author":"P \u00d6zen Kavas","year":"2023","journal-title":"Biomedical Signal Processing and Control"},{"issue":"6","key":"pone.0347840.ref058","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1016\/j.jacc.2024.10.100","article-title":"2024 ACC expert consensus decision pathway on practical approaches for arrhythmia monitoring after stroke: A Report of the American College of Cardiology Solution Set Oversight Committee","volume":"85","author":"Writing Committee","year":"2025","journal-title":"J Am Coll Cardiol"},{"key":"pone.0347840.ref059","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1038\/s41746-019-0136-7","article-title":"The use of photoplethysmography for assessing hypertension","volume":"2","author":"M Elgendi","year":"2019","journal-title":"NPJ Digit Med"},{"issue":"6","key":"pone.0347840.ref060","first-page":"503","article-title":"Large-scale validation of atrial fibrillation detection using wearable photoplethysmography","volume":"390","author":"HSI Apple","year":"2024","journal-title":"New England Journal of Medicine"},{"issue":"1","key":"pone.0347840.ref061","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The measurement of observer agreement for categorical data","volume":"33","author":"JR Landis","year":"1977","journal-title":"Biometrics"},{"issue":"2","key":"pone.0347840.ref062","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1080\/10255842.2020.1821192","article-title":"Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks","volume":"24","author":"A \u00c7\u0131nar","year":"2021","journal-title":"Comput Methods Biomech Biomed Engin"},{"key":"pone.0347840.ref063","first-page":"1","article-title":"Heart Failure Detection Using Photoplethysmography and Deep Learning","author":"M Tiosavljevi\u0107","year":"2025","journal-title":"2025 12th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN)"},{"key":"pone.0347840.ref064","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2025.3599704","article-title":"Optimized blood pressure prediction with a hybrid CNN\u2013SVR model using multiwavelength PPG","volume":"74","author":"C Botrugno","year":"2025","journal-title":"IEEE Trans Instrum Meas"},{"issue":"6","key":"pone.0347840.ref065","doi-asserted-by":"crossref","first-page":"101882","DOI":"10.1016\/j.disamonth.2025.101882","article-title":"The integration of artificial intelligence into clinical medicine: Trends, challenges, and future directions","volume":"71","author":"PS Aravazhi","year":"2025","journal-title":"Dis Mon"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0347840","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T17:38:34Z","timestamp":1778866714000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0347840"}},"subtitle":[],"editor":[{"given":"Zheng","family":"Yuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,5,15]]},"references-count":65,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5,15]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0347840","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,15]]}}}