Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions

Artificial intelligence in radiology

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The potential for artificial intelligence in healthcare

Davenport, T., & Kalakota, R. (2019) · Future Healthcare Journal · doi:10.7861/futurehosp.6-2-94 · Cited by 3,800 · see citing works

Revolutionizing healthcare: the role of artificial intelligence in clinical practice

Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., et al. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04698-z · Cited by 3,146 · see citing works

Artificial intelligence in healthcare: transforming the practice of medicine

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Precision medicine, AI, and the future of personalized health care

Johnson, K. B., Wei, W., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021) · Clinical and Translational Science · doi:10.1111/cts.12884 · Cited by 1,854 · see citing works

Artificial intelligence: A powerful paradigm for scientific research

Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., et al. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100179 · Cited by 1,726 · see citing works

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Vora, L. K., Gholap, A. D., Jetha, K. L., Singh, T. R. R., Solanki, H. K., & Chavda, V. P. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics15071916 · Cited by 1,152 · see citing works

Artificial intelligence for mental health and mental illnesses: an overview

Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H.-C., & Jeste, D. V. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1094-0 · Cited by 970 · see citing works

A Review of the Role of Artificial Intelligence in Healthcare

Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13060951 · Cited by 915 · see citing works

The impact of artificial intelligence in medicine on the future role of the physician

Ahuja, A. S. (2019) · PeerJ · doi:10.7717/peerj.7702 · Cited by 789 · see citing works

Reporting of artificial intelligence prediction models

Collins, G. S., & Moons, K. G. M. (2019) · The Lancet · doi:10.1016/s0140-6736(19)30037-6 · Cited by 701 · see citing works

Significance of machine learning in healthcare: Features, pillars and applications

Javaid, M., Haleem, A., Singh, R. P., Suman, R., & Rab, S. (2022) · International Journal of Intelligent Networks · doi:10.1016/j.ijin.2022.05.002 · Cited by 698 · see citing works

Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges

Huang, S., Yang, J., Fong, S. J., & Zhao, Q. (2019) · Cancer Letters · doi:10.1016/j.canlet.2019.12.007 · Cited by 695 · see citing works

Artificial intelligence for diabetes management and decision support: Literature review

Contreras, I., & Vehi, J. (2018) · Journal of Medical Internet Research · doi:10.2196/10775 · Cited by 560 · see citing works

Artificial Intelligence and Personalized Medicine

Schork, N. J. (2019) · Cancer treatment and research · doi:10.1007/978-3-030-16391-4_11 · Cited by 518 · see citing works

Evaluation of artificial intelligence techniques in disease diagnosis and prediction

Nia, N. G., Kaplanoğlu, E., & Nasab, A. (2023) · Discover Artificial Intelligence · doi:10.1007/s44163-023-00049-5 · Cited by 462 · see citing works

Role of artificial intelligence in patient safety outcomes: systematic literature review

Choudhury, A., & Asan, O. (2020) · JMIR Medical Informatics · doi:10.2196/18599 · Cited by 440 · see citing works

A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

El-Sappagh, S., Alonso, J. M., Islam, S. M. R., Sultan, A. M., & Kwak, K. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-82098-3 · Cited by 363 · see citing works

Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm

Krishnan, G., Singh, S., Pathania, M., Gosavi, S., Abhishek, S., Parchani, A., & Dhar, M. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1227091 · Cited by 326 · see citing works

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges

Guan, Z., Li, H., Liu, R., Cai, C., Liu, Y., Li, J., Wang, X., Huang, S., Wu, L., Liu, D., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101213 · Cited by 287 · cited 1× · see citing works

Artificial intelligence assists precision medicine in cancer treatment

Liao, J., Li, X., Gan, Y., Han, S., Rong, P., Wang, W., Li, W., & Zhou, L. P. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2022.998222 · Cited by 280 · see citing works

Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning

Makino, M., Yoshimoto, R., Ono, M., Itoko, T., Katsuki, T., Koseki, A., Kudo, M., Haida, K., Kuroda, J., Yanagiya, R., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-48263-5 · Cited by 250 · see citing works

Predictive analytics in health care: how can we know it works?

Van Calster, B., Wynants, L., Timmerman, D., Steyerberg, E. W., & Collins, G. S. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz130 · Cited by 223 · see citing works

Artificial intelligence supported patient self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised care

Barrett, M., Boyne, J. J., Brandts, J., Rocca, H. B., Maesschalck, L. D., Wit, K. D., Dixon, L., Eurlings, C., Fitzsimons, D., Golubnitschaja, O., et al. (2019) · The EPMA Journal · doi:10.1007/s13167-019-00188-9 · Cited by 211 · see citing works

Readmission prediction using deep learning on electronic health records

Ashfaq, A., Sant’Anna, A., Lingman, M., & Nowaczyk, S. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103256 · Cited by 194 · see citing works

Priorities for successful use of artificial intelligence by public health organizations: a literature review

Fisher, S., & Rosella, L. C. (2022) · BMC Public Health · doi:10.1186/s12889-022-14422-z · Cited by 190 · see citing works

Revolutionizing the Early Detection of Alzheimer’s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning

Vrahatis, A. G., Skolariki, K., Krokidis, M. G., Lazaros, K., Exarchos, T. P., & Vlamos, P. (2023) · Sensors · doi:10.3390/s23094184 · Cited by 178 · see citing works

Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review

Bertini, A., Salas, R., Chabert, S., Sobrevía, L., & Pardo, F. (2022) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2021.780389 · Cited by 160 · see citing works

Can ChatGPT provide intelligent diagnoses? A comparative study between predictive models and ChatGPT to define a new medical diagnostic bot

Caruccio, L., Cirillo, S., Polese, G., Solimando, G., Sundaramurthy, S., & Tortora, G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121186 · Cited by 158 · see citing works

Artificial Intelligence and Machine Learning in Prediction of Surgical Complications: Current State, Applications, and Implications

Hassan, A. M., Rajesh, A., Asaad, M., Nelson, J. A., Coert, J. H., Mehrara, B. J., & Butler, C. E. (2022) · The American Surgeon · doi:10.1177/00031348221101488 · Cited by 152 · see citing works

Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts

Volinsky-Fremond, S., Andani, S., Wolf, J. B., Dijkstra, J., Melsbach, S., Jobsen, J. J., Brinkhuis, M., Roothaan, S. M., Jurgenliemk-Schulz, I. M., Lutgens, L. C. H. W., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00210-2 · Cited by 148 · see citing works

Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms

Ma, M., Liu, R., Wen, C., Xu, W., Xu, Z., Wang, S., Wu, J., Pan, D., Zheng, B., Qin, G., et al. (2021) · European Radiology · doi:10.1007/s00330-021-08271-4 · Cited by 130 · see citing works

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

Rashid, J., Batool, S., Kim, J., Nisar, M. W., Hussain, A., Juneja, S., & Kushwaha, R. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.860396 · Cited by 104 · see citing works

Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study

Saux, P., Bauvin, P., Raverdy, V., Teigny, J., Verkindt, H., Soumphonphakdy, T., Debert, M., Jacobs, A., Jacobs, D., Monpellier, V. M., et al. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00135-8 · Cited by 100 · see citing works

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

Stafie, C. S., Șufaru, I.-G., Ghiciuc, C. M., Stafie, I.-I., Sufaru, E.-C., Solomon, S. M., & Hăncianu, M. (2023) · Diagnostics · doi:10.3390/diagnostics13121995 · Cited by 97 · see citing works

The prediction of surgical complications using artificial intelligence in patients undergoing major abdominal surgery: A systematic review

Stam, W. T., Goedknegt, L. K., Ingwersen, E. W., Schoonmade, L. J., Bruns, E. R. J., & Daams, F. D. (2021) · Surgery · doi:10.1016/j.surg.2021.10.002 · Cited by 95 · see citing works

Functional Outcome Prediction in Acute Ischemic Stroke Using a Fused Imaging and Clinical Deep Learning Model

Liu, Y., Yu, Y., Ouyang, J., Jiang, B., Yang, G., Ostmeier, S., Wintermark, M., Michel, P., Liebeskind, D. S., Lansberg, M. G., et al. (2023) · Stroke · doi:10.1161/strokeaha.123.044072 · Cited by 92 · see citing works

Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital

Brufau, S. R., Wyatt, K. D., Boyum, P., Mickelson, M. M., Moore, M., & Cognetta-Rieke, C. (2020) · Applied Clinical Informatics · doi:10.1055/s-0040-1715827 · Cited by 89 · see citing works

A Systematic Review of Transformer-Based Pre-Trained Language Models through Self-Supervised Learning

Kotei, E., & Thirunavukarasu, R. (2023) · Information · doi:10.3390/info14030187 · Cited by 89 · see citing works

A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening

Liu, H., Chen, Y., Zhang, Y., Wang, L., Luo, R., Wu, H., Wu, C., Zhang, H., Tan, W., Yin, H., et al. (2021) · European Radiology · doi:10.1007/s00330-020-07659-y · Cited by 88 · see citing works

Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk

Groos, D., Adde, L., Aubert, S., Boswell, L., de Regnier, R.-A. O., Fjørtoft, T., Gaebler-Spira, D. J., Haukeland, A., Loennecken, M. C., Msall, M. E., et al. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.21325 · Cited by 87 · see citing works

Development and testing of a random forest-based machine learning model for predicting events among breast cancer patients with a poor response to neoadjuvant chemotherapy

Jin, Y., Lan, A., Dai, Y., Jiang, L., & Liu, S. (2023) · European journal of medical research · doi:10.1186/s40001-023-01361-7 · Cited by 84 · see citing works

Artificial intelligence in prediction of mental health disorders induced by the COVID-19 pandemic among health care workers

Ćosić, K., Popović, S., Šarlija, M., Kesedžić, I., & Jovanović, T. (2020) · Croatian Medical Journal · doi:10.3325/cmj.2020.61.279 · Cited by 83 · see citing works

Prognostic prediction of hypertensive intracerebral hemorrhage using CT radiomics and machine learning

Xu, X., Zhang, J., Yang, K. G., Wang, Q., Chen, X., & Xu, B. (2021) · Brain and Behavior · doi:10.1002/brb3.2085 · Cited by 77 · see citing works

Towards computational solutions for precision medicine based big data healthcare system using deep learning models: A review

Thirunavukarasu, R., Doss, C. G. P., Ramanathan, G., Gopikrishnan, M., & Palanisamy, V. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106020 · Cited by 72 · see citing works

Machine Learning for Predicting Readmission Risk Among the Frail - Explainable AI for Healthcare

Mohanty, S. D., Lekan, D. A., McCoy, T. P., Jenkins, M., & Manda, P. (2021) · Patterns · doi:10.1016/j.patter.2021.100395 · Cited by 70 · see citing works

Artificial intelligence in critical illness and its impact on patient care: a comprehensive review

Saqib, M., Iftikhar, M., Neha, F., Karishma, F., & Mumtaz, H. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1176192 · Cited by 66 · see citing works

Development and validation of MRI‐based deep learning models for prediction of microsatellite instability in rectal cancer

Zhang, W., Yin, H., Huang, Z., Zhao, J., Zheng, H., He, D., Li, M., Tan, W., Tian, S., & Song, B. (2021) · Cancer Medicine · doi:10.1002/cam4.3957 · Cited by 63 · see citing works

Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of integrated approaches using carotid ultrasound

Jamthikar, A. D., Gupta, D., Saba, L., Khanna, N. N., Višković, K., Mavrogeni, S. I., Laird, J. R., Sattar, N. A., Johri, A. M., Pareek, G., et al. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104043 · Cited by 62 · see citing works

Advanced machine learning for predicting individual risk of flares in rheumatoid arthritis patients tapering biologic drugs

on behalf of the RETRO study group, Vodenčarević, A., Taşçılar, K., Hartmann, F., Reiser, M., Hueber, A. J., Haschka, J., Bayat, S., Meinderink, T., Knitza, J., et al. (2021) · Arthritis Research & Therapy · doi:10.1186/s13075-021-02439-5 · Cited by 62 · see citing works

Prediction of Coronary Stent Underexpansion by Pre-Procedural Intravascular Ultrasound–Based Deep Learning

Min, H., Ryu, D., Kang, S.-J., Lee, J., Yoo, J. H., Cho, H., Kang, D., Lee, P. H., Ahn, J., Park, D., et al. (2021) · JACC: Cardiovascular Interventions · doi:10.1016/j.jcin.2021.01.033 · Cited by 61 · see citing works

Clinical Application of Machine Learning-Based Artificial Intelligence in the Diagnosis, Prediction, and Classification of Cardiovascular Diseases

Shu, S., Ren, J., & Song, J. (2021) · Circulation Journal · doi:10.1253/circj.cj-20-1121 · Cited by 59 · see citing works

Machine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: results from the randomised phase III SAMIT trial

Sundar, R., Kumarakulasinghe, N. B., Chan, Y. H., Yoshida, K., Yoshikawa, T., Miyagi, Y., Rino, Y., Masuda, M., Guan, J., Sakamoto, J., et al. (2021) · Gut · doi:10.1136/gutjnl-2021-324060 · Cited by 57 · see citing works

Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study

Huang, L., Lin, W., Xie, D., Yu, Y., Cao, H., Liao, G., Wu, S., Yao, L., Wang, Z., Wang, M., et al. (2021) · European Radiology · doi:10.1007/s00330-021-08268-z · Cited by 54 · see citing works

Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model

Ma, X., Xia, L., Chen, J., Wan, W., & Zhou, W. Y. (2022) · European Radiology · doi:10.1007/s00330-022-09153-z · Cited by 52 · see citing works

Machine-learning based prediction of prognostic risk factors in patients with invasive candidiasis infection and bacterial bloodstream infection: a singled centered retrospective study

Li, Y., Wu, Y., Gao, Y., Niu, X., Li, J., Tang, M., Fu, C., Qi, R., Song, B., Chen, H., et al. (2022) · BMC Infectious Diseases · doi:10.1186/s12879-022-07125-8 · Cited by 50 · see citing works

Machine‐learning radiomics to predict early recurrence in perihilar cholangiocarcinoma after curative resection

Huan, Q., Hu, X., Zhang, J., Dai, H., He, Y., Zhao, Z., Yang, J., Xu, Z., Hu, X., & Chen, Z. (2020) · Liver International · doi:10.1111/liv.14763 · Cited by 48 · see citing works

Machine learning algorithm outperforms fibrosis markers in predicting significant fibrosis in biopsy‐confirmed NAFLD

Feng, G., Zheng, K. I., Li, Y., Rios, R. S., Zhu, P., Pan, X., Li, G., Ma, H., Tang, L., Byrne, C. D., et al. (2021) · Journal of Hepato-Biliary-Pancreatic Sciences · doi:10.1002/jhbp.972 · Cited by 48 · see citing works

Diagnostic Accuracy and Performance of Artificial Intelligence in Detecting Lung Nodules in Patients With Complex Lung Disease

Abadia, A. F., Yacoub, B., Stringer, N., Snoddy, M., Kocher, M., Schoepf, U. J., Aquino, G. J., Kabakus, I. M., Dargis, D. M., Hoelzer, P., et al. (2021) · Journal of Thoracic Imaging · doi:10.1097/rti.0000000000000613 · Cited by 47 · see citing works

Machine learning analysis for the noninvasive prediction of lymphovascular invasion in gastric cancer using PET/CT and enhanced CT-based radiomics and clinical variables

Fan, L., Li, J., Zhang, H., Yin, H., Zhang, R., Zhang, J., & Chen, X. (2022) · Abdominal Radiology · doi:10.1007/s00261-021-03315-1 · Cited by 47 · see citing works

AI-based clinical decision-making systems in palliative medicine: ethical challenges

Panfilis, L. D., Peruselli, C., Tanzi, S., & Botrugno, C. (2021) · BMJ Supportive & Palliative Care · doi:10.1136/bmjspcare-2021-002948 · Cited by 46 · see citing works

Dose prediction via distance-guided deep learning: Initial development for nasopharyngeal carcinoma radiotherapy

Yue, M., Xue, X., Wang, Z., Lambo, R. L., Zhao, W., Xie, Y., Cai, J., & Qin, W. (2022) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2022.03.012 · Cited by 46 · see citing works

Construction and validation of machine learning models for sepsis prediction in patients with acute pancreatitis

Liu, F., Yao, J. J., Liu, C., & Shou, S. (2023) · BMC Surgery · doi:10.1186/s12893-023-02151-y · Cited by 46 · see citing works

Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures

Cui, Y., Li, Z., Xiang, M., Han, D., Yin, Y., & Ma, C. (2022) · Radiation Oncology · doi:10.1186/s13014-022-02186-0 · Cited by 44 · see citing works

Imaging and artificial intelligence for progression of age-related macular degeneration

Romond, K., Alam, M. N., Kravets, S., de Sisternes, L., Leng, T., Lim, J. I., Rubin, D. L., & Hallak, J. (2021) · Experimental Biology and Medicine · doi:10.1177/15353702211031547 · Cited by 42 · see citing works

Machine learning v. traditional regression models predicting treatment outcomes for binge-eating disorder from a randomized controlled trial

Forrest, L. N., Ivezaj, V., & Grilo, C. M. (2021) · Psychological Medicine · doi:10.1017/s0033291721004748 · Cited by 42 · see citing works

Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer

Zhang, Y., Zhang, Z., Wei, L., & Wei, S. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.1008137 · Cited by 40 · see citing works

Comparison of Severity of Illness Scores and Artificial Intelligence Models That Are Predictive of Intensive Care Unit Mortality: Meta-analysis and Review of the Literature

Barboi, C., Tzavelis, A., & Muhammad, L. N. (2022) · JMIR Medical Informatics · doi:10.2196/35293 · Cited by 38 · see citing works

Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram

Shu, Z.-Y., Cui, S., Zhang, Y.-Q., Xu, Y.-Y., Hung, S.-C., Fu, L., Pang, P., Gong, X.-Y., & Jin, Q.-Y. (2020) · Journal of Nuclear Cardiology · doi:10.1007/s12350-020-02204-2 · Cited by 37 · see citing works

Clinical intelligence: New machine learning techniques for predicting clinical drug response

Turki, T., & Wang, J. T. L. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.12.017 · Cited by 33 · see citing works

Interpreting Deep Machine Learning Models: An Easy Guide for Oncologists

Amorim, J. P., Abreu, P. H., Fernández, A., Reyes, M., Santos, J., & Abreu, M. H. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3131358 · Cited by 33 · see citing works

Development and validation of machine learning-based predictive model for clinical outcome of decompression surgery for lumbar spinal canal stenosis

Yagi, M., Michikawa, T., Yamamoto, T., Iga, T., Ogura, Y., Tachibana, A., Miyamoto, A., Suzuki, S. O., Nori, S., Takahashi, Y., et al. (2022) · The Spine Journal · doi:10.1016/j.spinee.2022.06.008 · Cited by 32 · see citing works

Predicting outcomes in older ED patients with influenza in real time using a big data-driven and machine learning approach to the hospital information system

Tan, T.-H., Hsu, C., Chen, C., Hsu, S., Liu, T.-L., Lin, H., Wang, J., Liu, C., & Huang, C. (2021) · BMC Geriatrics · doi:10.1186/s12877-021-02229-3 · Cited by 31 · see citing works

Predicting N2 lymph node metastasis in presurgical stage I‐II non‐small cell lung cancer using multiview radiomics and deep learning method

Zhang, H., Liao, M., Guo, Q., Chen, J., Wang, S., Liu, S., & Xiao, F. (2022) · Medical Physics · doi:10.1002/mp.16177 · Cited by 31 · see citing works

Machine learning–based random forest for predicting decreased quality of life in thyroid cancer patients after thyroidectomy

Liu, Y. H., Jin, J., & Liu, Y. J. (2021) · Supportive Care in Cancer · doi:10.1007/s00520-021-06657-0 · Cited by 30 · see citing works

Using Machine-Learning for Prediction of the Response to Cardiac Resynchronization Therapy

Howell, S. J., Stivland, T., Steín, K. M., Ellenbogen, K. A., & Tereshchenko, L. G. (2021) · JACC. Clinical electrophysiology · doi:10.1016/j.jacep.2021.06.009 · Cited by 29 · see citing works

Prediction of repeated-dose intravenous ketamine response in major depressive disorder using the GWAS-based machine learning approach

Bao, Z., Zhao, X., Li, J., Zhang, G., Wu, H., Ning, Y., Li, M. D., & Yang, Z. (2021) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2021.04.014 · Cited by 27 · see citing works

Deep learning for predicting the risk of immune checkpoint inhibitor-related pneumonitis in lung cancer

Cheng, M., Lin, R., Bai, N., Zhang, Y., Wang, H., Guo, M.-Z., Duan, X., Zheng, J., Qiu, Z., & Zhao, Y. (2023) · Clinical Radiology · doi:10.1016/j.crad.2022.12.013 · Cited by 27 · see citing works

Using deep learning to predict survival outcome in non-surgical cervical cancer patients based on pathological images

Zhang, K., Sun, K., Zhang, C., Ren, K., Li, C., Shen, L., & Jing, D. (2023) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-022-04446-8 · Cited by 27 · see citing works

A DWI-based radiomics-clinical machine learning model to preoperatively predict the futile recanalization after endovascular treatment of acute basilar artery occlusion patients

Luo, Y., Sun, X., Kong, X., Tong, X., Xi, F., Yu, M., Miao, Z., & Ma, J. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110731 · Cited by 27 · see citing works

Improving palliative care with machine learning and routine data: a rapid review

Storick, V., O’Herlihy, A., Abdelhafeez, S., Ahmed, R., & May, P. (2019) · HRB Open Research · doi:10.12688/hrbopenres.12923.2 · Cited by 26 · see citing works

Using machine learning methods to predict 28-day mortality in patients with hepatic encephalopathy

Zhang, Z., Wang, J., Han, W., & Li, Z. (2023) · BMC Gastroenterology · doi:10.1186/s12876-023-02753-z · Cited by 26 · see citing works

Application of machine learning algorithms in electronic medical records to predict amputation-free survival after first revascularization in patients with peripheral artery disease

Liu, Y., Xue, J.-S., & Jiang, J. (2023) · International Journal of Cardiology · doi:10.1016/j.ijcard.2023.04.040 · Cited by 26 · see citing works

Deep learning-based postoperative visual acuity prediction in idiopathic epiretinal membrane

Wen, D., Yu, Z., Yang, Z., Zheng, C., Shao, Y., Ren, X., Gu, T., & Li, X. (2023) · BMC Ophthalmology · doi:10.1186/s12886-023-03079-w · Cited by 25 · see citing works

Deep Learning Radiomics Nomogram Based on Enhanced CT to Predict the Response of Metastatic Lymph Nodes to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer

Zhong, H., Wang, T., Hou, M., Liu, X., Tian, Y., Cao, S., Li, Z., Han, Z., Liu, G., Sun, Y., et al. (2023) · Annals of Surgical Oncology · doi:10.1245/s10434-023-14424-0 · Cited by 25 · see citing works

Development and validation of a combined nomogram model based on deep learning contrast-enhanced ultrasound and clinical factors to predict preoperative aggressiveness in pancreatic neuroendocrine neoplasms

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Combining machine learning with radiomics features in predicting outcomes after mechanical thrombectomy in patients with acute ischemic stroke

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Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes

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Machine Learning-Based Prediction of Atrial Fibrillation Risk Using Electronic Medical Records in Older Aged Patients

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Design of an interface to communicate artificial intelligence-based prognosis for patients with advanced solid tumors: a user-centered approach

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Evaluation of Multiple Machine Learning Models for Predicting Number of Anti-VEGF Injections in the Comparison of AMD Treatment Trials (CATT)

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Prediction of lung papillary adenocarcinoma-specific survival using ensemble machine learning models

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Using machine learning to predict gamma passing rate in volumetric‐modulated arc therapy treatment plans

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Development and validation of a prediction model based on machine learning algorithms for predicting the risk of heart failure in middle‐aged and older US people with prediabetes or diabetes

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Predicting efficacy of viloxazine extended-release treatment in adults with ADHD using an early change in ADHD symptoms: Machine learning Post Hoc analysis of a phase 3 clinical trial

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Twenty-eight-day in-hospital mortality prediction for elderly patients with ischemic stroke in the intensive care unit: Interpretable machine learning models

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A retrospective study using machine learning to develop predictive model to identify urinary infection stones in vivo

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Machine learning methods for accurately predicting survival and guiding treatment in stage I and II hepatocellular carcinoma

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Prediction of postoperative infection in elderly using deep learning-based analysis: an observational cohort study

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A Machine Learning–Based Prognostic Model for the Prediction of Early Death After Traumatic Brain Injury: Comparison with the Corticosteroid Randomization After Significant Head Injury (CRASH) Model

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Predicting major complications in patients undergoing laparoscopic and open hysterectomy for benign indications

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Prediction of pathological staging and grading of renal clear cell carcinoma based on deep learning algorithms

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Prediction of Postoperative Pathologic Risk Factors in Cervical Cancer Patients Treated with Radical Hysterectomy by Machine Learning

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Machine Learning Using Presentation CT Perfusion Imaging for Predicting Clinical Outcomes in Patients With Aneurysmal Subarachnoid Hemorrhage

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Predictive Models for Knee Pain in Middle-Aged and Elderly Individuals Based on Machine Learning Methods

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Predicting smoking cessation, reduction and relapse six months after using the Stop-Tabac app for smartphones: a machine learning analysis

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Machine Learning to Predict Risk of Relapse Using Cytologic Image Markers in Patients With Acute Myeloid Leukemia Posthematopoietic Cell Transplantation

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Magnetic resonance imaging-based deep learning model to predict multiple firings in double-stapled colorectal anastomosis

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Preoperative Extrapancreatic Extension Prediction in Patients with Pancreatic Cancer Using Multiparameter MRI and Machine Learning-Based Radiomics Model

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Preoperative prediction and histological stratification of intracranial solitary fibrous tumours by machine-learning models

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Clinical model of pulmonary metastasis in patients with osteosarcoma: A new multiple machine learning-based risk prediction

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Prediction of mortality in pneumonia patients with connective tissue disease treated with glucocorticoids or/and immunosuppressants by machine learning

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Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms

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Machine Learning-Based Development of Nomogram for Hepatocellular Carcinoma to Predict Acute Liver Function Deterioration After Drug-Eluting Beads Transarterial Chemoembolization

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Application of machine learning model in predicting the likelihood of blood transfusion after hip fracture surgery

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The Construction and Validation of a new Predictive Model for Overall Survival of Clear Cell Renal Cell Carcinoma Patients with Bone Metastasis Based on Machine Learning Algorithm

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Texture Features of Computed Tomography Image under the Artificial Intelligence Algorithm and Its Predictive Value for Colorectal Liver Metastasis

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A Machine Learning-Based Model to Predict In-Hospital Mortality of Lung Cancer Patients: A Population-Based Study of 523,959 Cases

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Prediction of treatment effect perception in cosmetics using machine learning

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Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration

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AI-driven innovations in Alzheimer's disease: Integrating early diagnosis, personalized treatment, and prognostic modelling

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Mapping the use of artificial intelligence in medical education: a scoping review

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Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare

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Advancements and applications of Artificial Intelligence in cardiology: Current trends and future prospects

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Current Bioinformatics Tools in Precision Oncology

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Revolutionizing Maternal Health: The Role of Artificial Intelligence in Enhancing Care and Accessibility

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Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery

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Redefining pedagogy with artificial intelligence: How nursing students are shaping the future of learning

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Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence

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Exploring geriatric nurses' perspectives on the adoption of AI in elderly care a qualitative study

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Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment

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Ethical Challenges in the Integration of Artificial Intelligence in Palliative Care

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Artificial intelligence in pharmacovigilance: a narrative review and practical experience with an expert-defined Bayesian network tool

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A State-of-the-Art Review of Artificial Intelligence (AI) Applications in Healthcare: Advances in Diabetes, Cancer, Epidemiology, and Mortality Prediction

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Precision Neuro-Oncology in Glioblastoma: AI-Guided CRISPR Editing and Real-Time Multi-Omics for Genomic Brain Surgery

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Data Ownership in the AI-Powered Integrative Health Care Landscape

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An Interpretable and Generalizable Machine Learning Model for Predicting Asthma Outcomes: Integrating AutoML and Explainable AI Techniques

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Medical imaging and radiation science students' use of artificial intelligence for learning and assessment

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Immunomodulatory therapy in non-infectious Uveitis: Current landscape, gaps, and future directions

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Writing the Future: Artificial Intelligence, Handwriting, and Early Biomarkers for Parkinson’s Disease Diagnosis and Monitoring

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Human-Computer Interaction: A Literature Review of Artificial Intelligence and Communication in Healthcare

Clay, T. J., Steel, Z. J. D. C., & Jacobs, C. (2024) · Cureus · doi:10.7759/cureus.73763 · Cited by 19 · see citing works

Diagnosis of nontuberculous mycobacterial infections using genomics and artificial intelligence-machine learning approaches: scope, progress and challenges

Murthy, M. K., Gupta, V. K., & Maurya, A. P. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1665685 · Cited by 17 · see citing works

Towards understanding cancer dormancy over strategic hitching up mechanisms to technologies

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Impact of artificial intelligence-based digital smile design on patient and clinician satisfaction and facial esthetic outcomes: A systematic review and meta-analysis

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The data scientist as a mainstay of the tumor board: global implications and opportunities for the global south

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Expanding horizons in esophageal squamous cell carcinoma: The promise of induction chemoimmunotherapy with radiotherapy

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From Echocardiography to CT/MRI: Lessons for AI Implementation in Cardiovascular Imaging in LMICs—A Systematic Review and Narrative Synthesis

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Management of polypharmacy through deprescribing in older patients: a review of the role of AI tools

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The Role of Artificial Intelligence in Pharmacy Practice and Patient Care: Innovations and Implications

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Prediction and detection of terminal diseases using Internet of Medical Things: A review

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Drug discovery in the context of precision medicine and artificial intelligence

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Large language models for improving cancer diagnosis and management in primary health care settings

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A systems approach to managing the risk of healthcare acquired infection in an acute hospital setting supported by human factors ergonomics, data science, data governance and AI

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Artificial Intelligence in Palliative Care: A Scoping Review of Current Applications, Challenges, and Future Directions

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Navigating Challenges in the Integration of Artificial Intelligence in Nursing Practice: An Integrative Literature Review

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Integrating biomarkers for hemostatic disorders into computational models of blood clot formation: A systematic review

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Comparative Study of Machine Learning and Deep Learning Models for Early Prediction of Ovarian Cancer

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Precision Medicine in Orthobiologics: A Paradigm Shift in Regenerative Therapies

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Artificial intelligence-based approaches for advance care planning: a scoping review

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Non-invasive biochemical sensing with AI-driven analytics: a comprehensive review of technologies, applications, and future directions

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Exploring the Current Applications of Artificial Intelligence in Orthopaedic Surgical Training: A Systematic Scoping Review

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Integrating Artificial Intelligence and Virtual Reality in Orthopedic Surgery: A Comprehensive Review

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Tomorrowland? Technical Innovation and Feedback on Writing

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Exploring an AI-First Healthcare System

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Leveraging Large Language Models in Gynecologic Oncology: A Systematic Review of Current Applications and ChallengesPreprint

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An Empirical Evaluation of Neural Network Architectures for 3D Spheroid Segmentation

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Harnessing Artificial Intelligence for the Diagnosis, Treatment and Research of Multiple Sclerosis

Patil, M. S., Lin, L. Y., Ford, R. K., James, E. J., Morton, S., Marsh‐Wakefield, F., Hawke, S. H., & Grau, G. E. (2025) · Sclerosis · doi:10.3390/sclerosis3020015 · Cited by 7 · see citing works

Immunotherapy biomarkers in brain metastases: insights into tumor microenvironment dynamics

Li, M., Zhang, Y., Yu, D., Yu, Y., & Ma, W. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1600261 · Cited by 7 · see citing works

The Philosophy of Artificial Intelligence in Healthcare: Facilitating a Human-Centred Paradigm to Optimize Healthcare Outcomes

Wei, H., Trepanier, S., Wei, T. L., Marshall, D. R., Wiley, R. D., & D'Agostino, D. C. (2025) · Healthcare Management Forum · doi:10.1177/08404704251394950 · Cited by 7 · see citing works

Interdisciplinary Development and Fine-Tuning of CARDIO, a Large Language Model for Cardiovascular Health Education in HIV Care: Tutorial

Rullo, R., Maatouk, A., Huang, T., Chen, J., Qiu, W., O’Connor, G., Womack, J. A., Sadak, T. I., Rodriguez, C., Carneiro, P. B., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/77053 · Cited by 8 · see citing works

The Frontiers of Smart Healthcare Systems

Lin, N., Paul, R., Guerra, S., Liu, Y., Doulgeris, J., Shi, M., Lin, M., Engeberg, E. D., Hashemi, J., & Vrionis, F. D. (2024) · Healthcare · doi:10.3390/healthcare12232330 · Cited by 6 · see citing works

Diagnosing facial synkinesis using artificial intelligence to advance facial palsy care

Knoedler, L., Festbaum, C., Dean, J., Baecher, H., de Lambertye, G., Maul, M., Schaschinger, T., Niederegger, T., Scheiflinger, A., Alfertshofer, M. G., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-08548-4 · Cited by 5 · see citing works

The use of artificial intelligence in the prevention and management of bleeding disorders: a systematic review

Mohamed, F. R. R., Aldabbagh, Z., Kalou, W., Hamsho, K., Aldabagh, A., Kalou, A., & Sajid, M. R. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1606788 · Cited by 5 · see citing works

μOR-ligand: target-aware view-based hybrid feature selection for μ-opioid receptor ligand functional classification

Ugurlu, S. Y. (2025) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-025-00686-1 · Cited by 5 · see citing works

Artificial Intelligence–Enhanced Multi-Algorithm R Shiny Application for Predictive Modeling and Analytics: Case Study of Alzheimer Disease Diagnostics

Wenzheng, H., Agyemang, E. F., Srivastav, S. K., Shaffer, J. G., & Kakraba, S. (2025) · JMIR Aging · doi:10.2196/70272 · Cited by 8 · see citing works

A Systematic Review of the Integration of Information Science, Artificial Intelligence, and Medical Engineering in Healthcare: Current Trends and Future Directions

Fotami, S. G. H., Mannuru, N. R., Bevara, R. V. K., & Mannuru, A. (2024) · InfoScience Trends · doi:10.61186/ist.202401.01.12 · Cited by 5 · see citing works

Analysis of Personalized Cardiovascular Drug Therapy: From Monitoring Technologies to Data Integration and Future Perspectives

Lin, R., Huang, Z., Liu, Y., & Zhou, Y. (2025) · Biosensors · doi:10.3390/bios15030191 · Cited by 5 · see citing works

Cybersecurity and Compliance in Clinical Trials: The Role of Artificial Intelligence in Secure Healthcare Management

Gomase, V. S., Ghatule, A. P., Sharma, R., & Sardana, S. (2025) · Reviews on Recent Clinical Trials · doi:10.2174/0115748871366467250413070850 · Cited by 5 · see citing works

Artificial Intelligence and Emerging Risks in Occupational Safety and Health

Baraza, X., & Torrent‐Sellens, J. (2026) · Encyclopedia · doi:10.3390/encyclopedia6010025 · Cited by 5 · see citing works

Stem Cell Exosomes for Osteoarthritis in Veterinary Medicine

Banu, S. A., Sharun, K., Emmanuel, R. S., Mamachan, M., Manjusha, K., Muthu, S., El‐Husseiny, H. M., Kumar, R., PAWDE, A. M., Dhama, K., et al. (2025) · Stem Cells International · doi:10.1155/sci/4888569 · Cited by 4 · see citing works

Metaverse Medicine

Kaur, J. (2024) · Advances in computer and electrical engineering book series · doi:10.4018/979-8-3693-5762-0.ch005 · Cited by 4 · see citing works

Exploring Young Adults' Attitudes Toward AI-Driven mHealth Apps: Qualitative Study

Aboueldahab, A., Damaschi, G., D’Αddario, M., & Steca, P. (2025) · JMIR Human Factors · doi:10.2196/76075 · Cited by 4 · see citing works

Towards a unified framework for single‐cell ‐omics‐based disease prediction through AI

Barberis, M., & Xie, J. (2025) · Clinical and Translational Medicine · doi:10.1002/ctm2.70290 · Cited by 4 · see citing works

Transforming the NHS through AI-driven solutions: a new era of digital health

Imam, M. A., Elgebaly, A. S., Zumla, A., Kolvekar, S. K., Ahmed, R., & Zumla, A. I. (2025) · Postgraduate Medical Journal · doi:10.1093/postmj/qgaf023 · Cited by 3 · see citing works

The Evolving Role of Artificial Intelligence in Medical Science: Advancing Diagnostics, Clinical Decision-Making, and Research

Singla, B., Afridi, S., Vayolipoyil, S., Ahmed, T., Afzaal, S., Saleem, K., Malik, M., & Qudsia, M. (2025) · Cureus · doi:10.7759/cureus.91514 · Cited by 3 · see citing works

Advancing Public Health Surveillance With Artificial Intelligence: A Systematic Review of Real‐Time Data Analytics and Disease Prediction

Islam, M. A., Hosen, A., Rony, M. K. K., Vanu, N., Bhuiyan, M. F. H., Tasnim, A. F., Tiwari, A., Yeasmin, S., & Manik, M. M. T. G. (2025) · Advances in Public Health · doi:10.1155/adph/2190224 · Cited by 5 · see citing works

Artificial intelligence in hemovigilance: A narrative review on advancing blood safety and monitoring systems

Lamem, M. F. H., & Sahid, M. I. (2025) · Digital Health · doi:10.1177/20552076251406306 · Cited by 4 · see citing works

Periodontitis Prediction Model Using Linked Electronic Health and Dental Records

Patel, J. S., Tellez, M., Katiyar, R. V., Al-Hebshi, N. N., Santana, R. B., Yucel, R. M., & Ismail, A. I. (2026) · JDR Clinical & Translational Research · doi:10.1177/23800844251408849 · Cited by 3 · see citing works

Application of AI and digital health tools in public health management of T2DM: from mechanism prediction to personalized treatment

Yu, C. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1756755 · Cited by 3 · see citing works

The problem with the ‘truth’: rethinking ground truth for artificial intelligence in endometriosis diagnosis

Deslandes, A., Zhang, Y., Leonardi, M., Chen, H., Carneiro, G., Avery, J., Condous, G. S., Knox, S., Hull, M. L., & the IMAGENDO Team (2026) · Human Reproduction · doi:10.1093/humrep/deag024 · Cited by 3 · see citing works

Enhancing hospital workforce planning, scheduling, and performance evaluation through an AI-driven human resource management system

Wang, Y., Zheng, P., Guan, Y., & Zhang, Q. (2026) · Scientific Reports · doi:10.1038/s41598-026-43102-w · Cited by 4 · see citing works

Decoding spontaneous intracerebral hemorrhage: mechanistic breakthroughs and disruptive revolution in pharmacological treatment

Chen, Y., Wu, G., Zhang, W., Zou, Z., Huang, L., Shi, H., Wang, Q. J., Chen, W., Luo, Z., Zhao, Z., et al. (2026) · Experimental & Molecular Medicine · doi:10.1038/s12276-026-01733-z · Cited by 3 · see citing works

Artificial Intelligence in Child and Adolescent Psychiatry: A Narrative Review of Recent Clinical Applications and Ethical Considerations

Cheng, S., Kim, E., Bhalodi, R. G., Um, T. S., Wong, D., & Yuen, E. Y. (2026) · Current Psychiatry Reports · doi:10.1007/s11920-026-01691-3 · Cited by 3 · see citing works

How artificial intelligence can enable personalized mesenchymal stem cell–based therapeutic strategies in systemic lupus erythematosus

Kumar, S. R., He, K. K., Lokanathan, Y., Gaurav, A., Yusoff, K., Macedo, M. F., & Bhassu, S. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1654117 · Cited by 4 · see citing works

Preclinical Identification of Poorly Controlled COPD: Patients with a Single Moderate Exacerbation Matter Too

Viejo, J. D. M., & Ros, F. M. N. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm14010022 · Cited by 4 · see citing works

Application of Generative AI to Enhance Obstetrics and Gynecology Research

Kawakita, T., Wong, M. S., Gibson, K. S., Gupta, M., Gimovsky, A. C., Moussa, H. N., Heo, H. J., & for the Society of Maternal-Fetal Medicine Clinical Informatics Committee (2025) · American Journal of Perinatology · doi:10.1055/a-2616-4182 · Cited by 3 · see citing works

AI-Enhanced Multi-Algorithm R Shiny App for Predictive Modeling and Analytics- A Case study of Alzheimer’s Disease Diagnostics (Preprint)Preprint

Kakraba, S., Wenzheng, H., Srivastav, S. K., & Shaffer, J. G. (2024) · doi:10.2196/preprints.70272 · Cited by 3 · see citing works

Development of a Machine Learning Tool for Home-Based Assessment of PeriodontitisPreprint

Zhu, X., Wu, X., Zhu, L., Uzel, N., Zavras, A. I., Tu, Q., & Chen, J. (2025) · medRxiv · doi:10.1101/2025.03.10.25323689 · Cited by 3 · see citing works

Artificial Intelligence (AI) Models for Cardiovascular Disease Risk Prediction in Primary and Ambulatory Care: A Scoping ReviewPreprint

Provost, C., Broughan, J., McCombe, G., Kelly, M. O., Ledwidge, M. T., Cullen, W., & Gallagher, J. (2025) · medRxiv · doi:10.1101/2025.03.21.25324379 · Cited by 3 · see citing works

Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data

Oei, C. W., Ng, E. Y. K., Ng, M. H. S., Chan, Y. M., Subbhuraam, V., Chan, L. G., & Acharya, U. R. (2025) · Bioengineering · doi:10.3390/bioengineering12050517 · Cited by 2 · see citing works

Recent Trends and Future Prospects in Metallic Implants for Biomedical Applications

Kandasamy, J., Arulvel, S., Jeyapandiarajan, P., Murugan, M., & Riju, R. P. (2025) · Springer tracts in additive manufacturing · doi:10.1007/978-3-031-90807-1_8 · Cited by 2 · see citing works

Data-driven identification of key predictors of uncontrolled hypertension: A cross-sectional study

Mendoza‐Cano, O., Trujillo, X., Huerta, M., Ríos‐Silva, M., Lugo-Radillo, A., Bricio‐Barrios, J. A., Benites-Godínez, V., Cuevas-Arellano, H. B., Uribe-Ramos, J. M., Solano-Barajas, R., et al. (2025) · PLoS ONE · doi:10.1371/journal.pone.0331565 · Cited by 2 · see citing works

Cognitive Integration for Hybrid Collective Agency

Wang, R. (2025) · Philosophies · doi:10.3390/philosophies10050103 · Cited by 2 · see citing works

Trajectories of health conditions predict cardiovascular disease risk among middle-aged and older adults: a national cohort study

Li, W., Liu, T., Yuanjia, H., Zhou, H., Liu, Y., Zeng, H., Zhang, Y., Zhang, C., Li, K., Hu, Z., et al. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1657587 · Cited by 2 · see citing works

Ethical, data security, and resource allocation considerations in AI integration for healthcare during Hajj: task force insights and future directions

Sulaiman, K. A. A., Alharthi, A. F., Alqahtani, R. A., Aljouie, A., Khan, A. A., Al-Jedai, A. H., Almoeen, A., Alshennawi, M. A., Badreldin, H. A., Alnasser, L. A., et al. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106123 · Cited by 2 · see citing works

Knockoff-ML: a knockoff machine learning framework for controlled variable selection and risk stratification in electronic health record data

Wang, Q., Li, L., & Yang, Y. A. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02102-2 · Cited by 2 · see citing works

Dijital Sağlık Teknolojilerinin Sürdürülebilir Sağlık Sistemlerine Katkısı: Türkiye Odaklı Bir İnceleme

Kınış, Z., Denizli, F., & Boztosun, D. (2026) · İstanbul Gelişim Üniversitesi Sosyal Bilimler Dergisi · doi:10.17336/igusbd.1715799 · Cited by 3 · see citing works

The Role of Explanations in AI-Generated Alerts: Qualitative Study of Clinical Views on Explainable AI in Predictive Tools

Rahman, J. S., Delaforce, A., Bradford, D., Li, J., Magrabi, F., Cook, D., & Brankovic, A. (2026) · JMIR Human Factors · doi:10.2196/81460 · Cited by 2 · see citing works

Optical Sensing Platforms for Detecting Alzheimer's Disease Blood Biomarkers: From Laboratory Assays to Clinical Validation

Sun, X., Xia, X., Liu, Y., Qi, X., & Xu, T. (2026) · Advanced Sustainable Systems · doi:10.1002/adsu.70442 · Cited by 2 · see citing works

AI Governance Capacity in Public Administration

Özsavaş, M. G., & Özsavaş, E. E. (2026) · International Journal of Public Administration in the Digital Age · doi:10.4018/ijpada.406076 · Cited by 4 · see citing works

Reining in Unbridled AI Enthusiasm: Protecting the Integrity of Rehabilitation Science and Clinical Care

Rabinowitz, A. R., Trauger, M., & Williams, M. (2026) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2026.04.033 · Cited by 2 · see citing works

Trusting the Algorithm or Trusting the Nurse? Critical Care Nurses' Experiences of Automation Bias and Professional Autonomy in AI ‐Assisted Early Warning

Samy, R., Ramadan, O. M. E., & Elsayed, G. E. A. (2026) · Nursing in Critical Care · doi:10.1111/nicc.70620 · Cited by 2 · see citing works

Navigating the Genetic Landscape: A Comprehensive Review of Novel Therapeutic Strategies for Retinitis Pigmentosa Management

Pawar, Y. B., & Thool, A. R. (2024) · Cureus · doi:10.7759/cureus.67046 · Cited by 2 · see citing works

Healthcare and AI frontiers: thematic insights and domains categorization

Forciniti, A., & Santelli, F. (2025) · Quality & Quantity · doi:10.1007/s11135-025-02173-z · Cited by 2 · see citing works

Comparing predictor value of lactic acid, base deficit, erythrocyte sedimentation rate, and C‐reactive protein in severe burn outcome

Khalili, A., Moghadam, A. D., Mobayen, M., Rimaz, S., & Moghadam, F. D. (2025) · Hong Kong Journal of Emergency Medicine · doi:10.1002/hkj2.70022 · Cited by 2 · see citing works

Artificial intelligence in health professions education: A state-of-the-art meta-reviewPreprint

Hossain, M. M., Hossain, P., Roy, T. J., Das, J., Tasnim, S., Ma, P., & Liaw, W. R. (2025) · medRxiv · doi:10.1101/2025.10.20.25338371 · Cited by 3 · see citing works

Systematic use of protein free energy changes for classifying variants of uncertain significance: the case of IFT140 in Mainzer-Saldino Syndrome

Gajardo, M., Guerrero, J. L., Poblete, B., Bayyad, E., Castro, I., Maturana, J., Tobar, J., Faùndes, V., & Krall, P. M. (2025) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2025.1561380 · Cited by 1 · see citing works

Ethical Considerations in Automated Healthcare

Shafik, W. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-1022-0.ch016 · Cited by 1 · see citing works

Metabolic network remodeling and AI-driven precision diagnostics in geriatric Parkinson's disease: Advances in multimodal imaging

Liu, J., Yang, P., Wang, Z., Yang, K., Wang, K., Ainivar, K., Chu, K., & Yang, X. (2025) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2025.105983 · Cited by 1 · see citing works

AI use in clinical prediction

Khan, I., Choudhury, A. D., Alam, I., Salahuddin, M., Mathur, S. H., Siddiqui, F., & Badar, M. S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44915-4.00013-7

Artificial intelligence in disease prediction and diagnosis: a systematic literature review

Harshith, R., Pranathi, K., Navya, B., Kavitha, T., Sai Keerthi, K., Devi Vara Prasad, R., & Hema Kumari, N. (2025) · International Journal of Current Innovations in Advanced Research · doi:10.47957/ijciar.v8i3.207

A Systematic Review on Artificial Intelligence in Cancer Prediction

Gautam, N., Sharma, M., Aggarwal, N., & Grover, S. (2025) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2025.v07i05.55405

AI‐Powered Clinical Decision Support Systems in Disease Diagnosis, Treatment Planning, and Prognosis: A Systematic Review

Nojomi, M., Babaee, E., Rampisheh, Z., Benis, M. R., Soheyli, M., & Raz, N. R. (2025) · Medical Journal of the Islamic Republic of Iran · doi:10.47176/mjiri.39.81 · Cited by 6 · see citing works

Artificial Intelligence in Predictive Healthcare: A Systematic Review

Al-Nafjan, A., Aljuhani, A., Alshebel, A., AlHarbi, A., & Alshehri, A. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14196752 · Cited by 56 · see citing works