The DOI matches its Crossref record; 143 of the 143 references it cites were found in a registry or an index.
RETRACTED ARTICLE: Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda
Open accessAbstract
Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“By leveraging multimodal technologies, AI agents can simultaneously analyze various data sources—such as medical imaging, electronic health records, and genetic data—to assist in diagnosis, predict patient outcomes, and recommend personalized treatment plans.46,47,48”
From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [48]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The complexity of plant genomes and gene interactions complicates the breeding process, as one gene’s expression can influence another’s expression, leading to unintended phenotypic outcomes (Kumar, 2023).”
From Emerging applications of gene editing technologies for the development of climate-resilient crops (2025) · cites this paper as Kumar, 2023
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Multi-omics approaches have identified targets like OsBADH2 and OsMPK2 in rice that could enhance drought resilience (Kumar, 2023).”
From Emerging applications of gene editing technologies for the development of climate-resilient crops (2025) · cites this paper as Kumar, 2023
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Targeting ABA signaling genes, such as PYL receptors, has improved drought tolerance across various species (Kumar, 2023).”
From Emerging applications of gene editing technologies for the development of climate-resilient crops (2025) · cites this paper as Kumar, 2023
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Kather, J. N., Pearson, A. T., Halama, N., Jäger, D., Krause, J., Loosen, S. H., Marx, A., Boor, P., Tacke, F., Neumann, U. P., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0462-y · Cited by 1,458
Machine learning and data mining methods in diabetes research
Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.12.005 · Cited by 1,445
Real-time cryo-electron microscopy data preprocessing with Warp
Tegunov, D., & Cramer, P. (2019) · Nature Methods · doi:10.1038/s41592-019-0580-y · Cited by 1,292
Artificial Intelligence in Precision Cardiovascular Medicine
Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.03.571 · Cited by 1,126
Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning
Minaee, S., Kafieh, R., Milan Sonka, A., Yazdani, S., & Soufi, G. J. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101794 · Cited by 1,056
Skaane, P., Bandos, A. I., Gullien, R., Eben, E. B., Ekseth, U., Haakenaasen, U., Izadi, M., Jebsen, I. N., Jahr, G., Krager, M., et al. (2013) · Radiology · doi:10.1148/radiol.12121373 · Cited by 1,015
Arrhythmia detection using deep convolutional neural network with long duration ECG signals
Yıldırım, Ö., Pławiak, P., Tan, R., & Acharya, U. R. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.09.009 · Cited by 886
Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM
Srinivasu, P. N., SivaSai, J. G., Ijaz, M. F., Bhoi, A. K., Kim, W., & Kang, J. J. (2021) · Sensors · doi:10.3390/s21082852 · Cited by 763
Self-citationJo, T., Nho, K., & Saykin, A. J. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00220 · Cited by 724
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 724
Research works printed but not linked: 20
- Babu BS, Likhitha V, Narendra I, Harika G (2019) Prediction and detection of heart attack using machine learning and internet of things. J Comput Sci 4:105–108
- Farokhzad M, Ebrahimi L (2016) A novel adapter neuro fuzzy inference system for the diagnosis of liver disease. J Acad Res Comput Eng 1:61–66
- Fukuda M, Inamoto K, Shibata N, Ariji Y, Kutsana S (2019) Evaluation of an artificial system for detecting vertical root fracture on panoramic radiography. Oral Radiol 36:1–7
- George A, Badagabettu S, Berra K, George L, Kamath V, Thimmappa L (2018) Prevention of cardiovascular disease in India. Clin Prev Cardiol 7:72–77. https://doi.org/10.4013/JCPC.JCPC_31_17 · DOI
- Isravel DP, Silas SVPD (2020) Improved heart disease diagnostic IoT model using machine learning techniques. Neuroscience 9:4442–4446
- Khaled E, Naseer S, Metwally N (2018) Diagnosis of hepatititus virus using arificial neural network. J Acad Pedagog Res 2:1–7
- Kolkur MS, Kalbande DR, Kharkar V (2018) Machine learning approaches to multi-class human skin disease Ddetection. Innov Healthc Tech 14:29–39
- Memon M, Li J, Haq A, Memon M (2019) Breast cancer detection in the Iot health environment using modified recursive feature selection. Wirel Commun Mob 2019:19
- Musleh M, Alajrami E, Khalil A, Nasser B, Barhoom A, Naser S (2019) Predicting liver patients using artificial neural network. J Acad Inf Syst Res 3:1–11
- Naser S, Naseer I (2019) Lung cancer detection using artificial neural network. J Eng Inf Syst 3:17–23
- Nasser I, Naser S et al (2019) Predicting tumor category using artificial neural network. Eng Inf Technol 3:1–7
- Ostovar A, Chimeh E, Fakoorfard Z (2020) The diagnostic value of CT scans in the process of diagnosing COVID-19 in medical centers. Health Technol Assess Act 4:1–7
- Panicker RO, Kalmady KS, Rajan J, Sabu MK (2018) Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods. Biocybern Biomed Eng 38(3):691–699. https://doi.org/10.1016/j.bbe.2018.05.007 · DOI
- Patel SB (2016) Heart disease using machine learning and data minig techniques. Health Technol 10:1137–1144
- Romanini J, Barun L, Martins M, Carrard V (2020) Continuing education activities improve dentists self efficacy to manage oral mucosal lesions and oral cancer. Eur J Dent Educ 25:28–34
- Saranya E, Maheswaran T (2019) IOT based disease prediction and diagnosis system for healthcare. Healthc Technol 7:232–237
- Singh J, Tripathy A, Garg P, Kumar A (2020) Lung Alzheimer detection using anti-aliased convolutional networks networks. Proc Comput Sci 173:281–290. https://doi.org/10.1016/j.eswa.2018.07.014 · DOI
- Srinivasu PN, Ahmed S, Alhumam A, Kumar AB, Ijaz MF (2021) An AW-HARIS based automated segmentation of human liver using CT images. Comput Mater Contin 69(3):3303–3319
- Thai DT, Minh QT, Phung PH (2017) Toward an IoT-based expert system for heart disease diagnosis. In: Modern artificial intelligence and cognitive science conference, vol 1964, pp 157–164
- Vasal S, Jain S, Verma A (2020) COVID-AI: an artificial intelligence system to diagnose COVID 19 disease. J Eng Res Technol 9:1–6
Cited by
Sorted by citations
Items 501–600 of 999
Smart Healthcare: Harnessing the Power of Machine Learning for Predictive Analysis
Barhate, A., Kumar, P., Verma, P., Jikar, N., Tale, A., & Hikre, V. (2024) · Parul International Conference on Engineering and Technology (PICET) · doi:10.1109/picet60765.2024.10716168 · Cited by 7
Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057396 · Cited by 8
„KI“ in der Rechtsmedizin – von der Forschung in die Praxis: Welche Herausforderungen ergeben sich?
Rüther, M., Eickhoff, S. B., König, L. M., Ritz, S., & Schäffer, B. (2025) · Rechtsmedizin · doi:10.1007/s00194-025-00742-y · Cited by 3
Park, H. J., Ham, S., Shim, E., Suh, D. H., & Kim, J. G. (2025) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.24.01530 · Cited by 3
Akbar, W., Li, S., Abdullah, A., Hussain, A., Attar, R. W., Hussain, T., Alhazmi, A. H., & Alsagri, R. (2025) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001425400051 · Cited by 3
Meng, H., Tang, Y., Qi, Y., Zhou, Q., He, L., & Chen, S. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1638904 · Cited by 3
Caraka, R. E., Supardi, K., Gondhowiardjo, S. A., Isnaniawardhani, V., Gio, P. U., Chen, R.-C., & Pardamean, B. (2025) · Journal of Healthcare Leadership · doi:10.2147/jhl.s550933 · Cited by 4
He, Y., Yu, J., Xie, Y., Wu, Y., Wang, L., Ren, J., Zhang, Z., Yu, T., Jiang, S., Shan, H., et al. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-07297-2 · Cited by 3
Decoding the plasma proteome: Advancing precision medicine in cardiovascular health
Cabrera-Fuentes, H. A., Liehn, E. A., & Al-Suhaimi, E. A. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104584 · Cited by 4
Kermansaravi, M., Salminen, P., Prager, G., & COHEN, R. V. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0001132 · Cited by 3
Innovating for tomorrow: industry 4.0’s role in sustainable healthcare
Ferreira, A. R. O., & Fernandes, T. A. (2026) · Health and Technology · doi:10.1007/s12553-025-01050-x · Cited by 3
Adesina, A. F., Olorunfemi, B. O., Adeniji-Sofoluwe, A. T., Otuechere, C. A., Olopade, F., & Aribisala, B. S. (2026) · iLiver · doi:10.1016/j.iliver.2026.100225 · Cited by 3
Privacy-preserving cloud-based dermatological image processing for medical applications: a review
Chen, S., Zhang, X., Liu, E., Xiong, Y., Wang, L., Gu, X., Wang, Z., Guo, J., & Luo, T. (2026) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-026-00886-6 · Cited by 4
IoB-Based Intelligent Healthcare System for Disease Diagnosis in Humans
Shalu, S., Saini, N., Pooja, P., & Singh, D. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-6544-1_43 · Cited by 3
Afzal, H., Jahangir, T., Mei, Y., Madden, A., Sarker, A., & Kim, S. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1309490 · Cited by 3
Impact of Post-Use Satisfaction on Re-Purchase Intentions for AI-Driven Healthcare Smart Wearables
Gupta, M., Chopra, P., & Pathak, N. (2024) · Advances in business information systems and analytics book series · doi:10.4018/979-8-3693-1842-3.ch016 · Cited by 3
Ardiyansa, S. A., Maharani, N. C., Anam, S., & Julianto, E. (2024) · BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN · doi:10.30598/barekengvol18iss2pp1053-1066 · Cited by 3
Szeremeta, M., Janica, J., & Niemcunowicz‐Janica, A. (2024) · Archives of Forensic Medicine and Criminology · doi:10.4467/10.4467/16891716amsik.24.005.19650 · Cited by 3
Lankarani, K. B., Aboulpor, N., Boostani, R., & Saeian, S. (2024) · Neurogastroenterology & Motility · doi:10.1111/nmo.14931 · Cited by 3
Ekpar, F. E. (2024) · MOLECULAR SCIENCES AND APPLICATIONS · doi:10.37394/232023.2024.4.13 · Cited by 3
AI Technologies in Reducing Hospital Readmission for Chronic Diseases: A Recommended Framework
Farid, F., Bello, A., Ahamed, F., Hossain, F., Sabrina, F., & Sohail, S. (2024) · IEEE International Conference on Future Machine Learning and Data Science (FMLDS) · doi:10.1109/fmlds63805.2024.00022 · Cited by 3
Geraci, J., Rao, P., Grandinetti, C., Qorri, B., Nadolny, P., Ayalew, K., Bregnhøj, L., Edwards, L., Hofmann, K., Khozin, S., et al. (2025) · Journal of the Society for Clinical Data Management · doi:10.47912/jscdm.426 · Cited by 4
Maternal exposure to rubella infection elevates risk of congenital rubella syndrome (CRS)
Pitchaikani, S., Govindan, P., & Shakila, H. (2025) · International review of neurobiology · doi:10.1016/bs.irn.2025.04.008 · Cited by 3
Segall, R. S., & Sankarasubbu, V. (2022) · International Journal of Artificial Intelligence and Machine Learning · doi:10.4018/ijaiml.313574 · Cited by 3
Buhl, L. M., & Pattanayak, S. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020679 · Cited by 3
Transforming Healthcare System through AI Sustainability: Chatbots and Emergency Assistance
Deorukhkar, U. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.54247 · Cited by 3
K-Nearest Neighbor Algorithm for Efficient Heart Disease Classification System
Abdalkafor, A. S., & Alheeti, K. M. A. (2023) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese58274.2023.10099808 · Cited by 3
Intelligent Wearable Healthcare Monitoring Framework
Choudhury, A., & Sarma, K. K. (2023) · Advances in systems analysis, software engineering, and high performance computing book series · doi:10.4018/978-1-6684-8602-3.ch008 · Cited by 3
Artificial intelligence and rehabilitation: what’s new and promising
Marks, R. (2023) · International Physical Medicine & Rehabilitation Journal · doi:10.15406/ipmrj.2023.08.00348 · Cited by 3
Alpaullivarez, A. A., Aurelia, M., Monica, S., & Suri, P. A. (2023) · International Conference on Artificial Intelligence and Data Sciences (AiDAS) · doi:10.1109/aidas60501.2023.10284675 · Cited by 3
Sharma, R., Gulati, A., & Chopra, K. (2023) · Springer eBooks · doi:10.1007/978-981-99-6472-7_2 · Cited by 4
Artificial intelligence applications in disease diagnosis and treatment: recent progress and outlook
Goswami, M., Sachan, P., & Goswami, K. (2023) · Research in Pharmacy · doi:10.25081/rip.2023.v13.8632 · Cited by 3
Utilization of Artificial Intelligence for Social Media and Gaming Moderation
Saleous, H., Gergely, M., & Shuaib, K. (2023) · International Conference on Innovations in Information Technology · doi:10.1109/iit59782.2023.10366468 · Cited by 3
Karunananthan, S., Rahgozar, A., Hakimjavadi, R., Yan, H., Dalsania, K. A., Bergman, H., Bishwajit, G., Laplante, J., McCutcheon, T., McIsaac, D. I., et al. (2023) · BMJ Open · doi:10.1136/bmjopen-2023-076918 · Cited by 5
Advancements in Image Classification for Malaria Diagnosis
Jethwa, A., Sanghvi, M., Kumar, Y., & Shah, V. (2023) · International Conference on Recent Advances in Science and Engineering Technology (ICRASET) · doi:10.1109/icraset59632.2023.10420067 · Cited by 3
Self-citationLaxmibai, & Patil, V. (2023) · International Conference on Integrated Intelligence and Communication Systems (ICIICS) · doi:10.1109/iciics59993.2023.10421028 · Cited by 3
Sloss, E. A., McPherson, J. P., Beck, A. C., Guo, J., Scheese, C. H., Flake, N., Chalkidis, G., & Staes, C. J. (2024) · JCO Clinical Cancer Informatics · doi:10.1200/cci.23.00187 · Cited by 3
Leveraging Artificial Intelligence for Enhanced Healthcare Diagnostics: Opportunities and Challenges
Ahmed, S. K. M. (2024) · International Journal of Science and Research (IJSR) · doi:10.21275/sr24316150834 · Cited by 3
Zhao, N., Bennett, D., Baskozos, G., & Barry, A. M. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae156 · Cited by 3
A combination between transfer learning models and UNet++ for COVID-19 diagnosis
Nguyen, H. T., Nguyen, D. T., Tran, T. T., & Pham, N. H. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20392-8 · Cited by 3
Deevi, D. P., Allur, N. S., Dondapati, K., Chetlapalli, H., Kodadi, S., & Ajao, L. A. (2024) · International Conference on Emerging Research in Computational Science (ICERCS) · doi:10.1109/icercs63125.2024.10895279 · Cited by 3
Wang, F., & Zheng, S. (2025) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.1127 · Cited by 3
Suarez‐Barcena, P. D., Parra‐Perez, A. M., Martin-Lagos, J., Gallego‐Martinez, A., Lopez-Escamez, J. A., & Perez‐Carpena, P. (2025) · Headache The Journal of Head and Face Pain · doi:10.1111/head.14924 · Cited by 3
HFSA: hybrid feature selection approach to improve medical diagnostic system
Rabie, A. H., Aldawsari, M. F., Saleh, A. I., Saraya, M. S., & Rashad, M. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2764 · Cited by 3
Interdisciplinary framework for the development of artificial intelligence solutions in one health
Shastry, K. A. (2025) · Progress in Artificial Intelligence · doi:10.1007/s13748-025-00369-w · Cited by 3
Population health management genomic new-born screens and multi-omics intercepts
Henry, J. A. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1496942 · Cited by 3
Applications of artificial intelligence and digital holography in biomedical microscopy
Dar, A. U., Ahanger, A. B., Rasool, M., Singh, M., Assad, A., Macha, M. A., Aalam, S. W., & Ahanger, A. N. (2025) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-025-03070-2 · Cited by 4
Automated intelligent online healthcare ontology Integration
Maghawry, N. E., Emara, K., Shaaban, E., & Ghoniemy, S. A. (2022) · International Conference on Computing and Informatics (ICCI) · doi:10.1109/icci54321.2022.9756070 · Cited by 3
Bender, B. F., & Berry, J. A. (2023) · Advances in Geriatric Medicine and Research · doi:10.20900/agmr20230002 · Cited by 5
Policy implications of artificial intelligence (AI)
Bhatnagar, A., & Gajjar, D. (2024) · doi:10.58248/pn708 · Cited by 3
The impact of artificial intelligence on medical diagnostics: A letter to the editor
Imtiaz, S., & Jillani, S. A. Q. (2024) · Journal of the Pakistan Medical Association · doi:10.47391/jpma.10668 · Cited by 3
UltraWeak: Enhancing Breast Ultrasound Cancer Detection with Deformable DETR and Weak Supervision
Khan, U., Nawaz, U., & Saddik, A. E. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73376-5_14 · Cited by 2
Deep Federated Learning for Real-Time Decision Making in Autonomous Vehicles
Dhage, V. N., & Ponnusamy, S. (2025) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3373-0563-9.ch014 · Cited by 2
Anusha, S., & Manjith, R. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107930 · Cited by 1
The Role of Artificial Intelligence in Advancing Lifestyle Medicine Research
Chan, K. H. K., & Liu, S. (2025) · Springer eBooks · doi:10.1007/978-3-031-82888-1_17 · Cited by 1
Fukuda, Y., & Fukuda, K. (2025) · JMIR Human Factors · doi:10.2196/64192 · Cited by 2
Natural products proposed for the management of Huntington’s disease (HD): a comprehensive review
Salman, A., Eid, A. H., Khalaf, S. S., El‐Dessouki, A. M., El‐Shiekh, R. A., & Aly, S. H. (2025) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/s00210-025-04444-w · Cited by 2
Sánchez-Escobar, N., Romero-Tlalolini, M. D. L. Á., Rosas‐Vargas, H., Jiménez‐Hernández, E., Núñez-Enríquez, J. C., Rangel-López, A., Sánchez-López, J. M., Rojo-Serrato, D., Mata, A. M. J., Aguilar, E. A. M., et al. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26147003 · Cited by 2
AI-driven therapeutics and novel interventions in migraine: a systematic review of emerging trends
Y, S. S. T., Sasanka, K. K., Meena, B., Pareek, S., Singh, M. P., Prasad, M. S. D., & Sandeep, M. (2025) · The Egyptian Journal of Neurology Psychiatry and Neurosurgery · doi:10.1186/s41983-025-01021-z · Cited by 2
Yoon, J., Zajac, P., Pococke, L., Jicol, A., Clarke, C., O'Neill, E. J., Petrini, K., Lutteroth, C., & Jicol, C. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3616748 · Cited by 3
Picone, C., Fusco, R., Granata, V., Ottaiano, A., Brunese, M. C., Mormile, R., & Petrillo, A. (2025) · Journal of Medical Imaging and Interventional Radiology · doi:10.1007/s44326-025-00085-8 · Cited by 2
Srimaharaj, W. (2026) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111440 · Cited by 2
Artificial intelligence robots for mental health applications: a scoping review
Dong, P., Zhang, S., Zheng, X., Yuan, W., Wu, L., & Chen, Y. (2026) · Psychiatry Research · doi:10.1016/j.psychres.2026.117059 · Cited by 2
Jadhav, A., Awale, R. N., & Ingle, R. R. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110165 · Cited by 2
Taheri, Z., Tajdari, M., Ghadikolaei, Y. H., Eftekhari, H., Dodangeh, S., Jamshidi, P., Andarzbakhsh, K., & Rezazadeh, A. (2026) · Journal of Diabetes and Metabolic Disorders · doi:10.1007/s40200-025-01817-z · Cited by 2
AI image-based diagnosis systems: how to implement them?
Ablameyko, M., & Ablameyko, S. (2023) · Journal of Digital Health · doi:10.55976/jdh.22023113912-21 · Cited by 2
A Survey of Bias in Healthcare: Pitfalls of Using Biased Datasets and Applications
Velichkovska, B., Denkovski, D., Gjoreski, H., Kalendar, M., & Osmani, V. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-35314-7_50 · Cited by 3
Deep Learning - Recent Findings and Research
Domínguez-Morales, M., Civit-Masot, J., Munoz-Saavedra, L., & Damaševičius, R. (2023) · Artificial intelligence · doi:10.5772/intechopen.107725 · Cited by 5
Technologies and Innovations for Smart Health Care System in Society 5.0
Yadav, N., Bala, S., Thakur, L., Yadav, N., & Yadav, D. (2023) · Springer eBooks · doi:10.1007/978-981-99-6472-7_10 · Cited by 2
Arivanantham, S., Sekar, P., Sundhararajan, N., Nagalingam, Y., & Raju, S. K. (2023) · World Journal of Biology Pharmacy and Health Sciences · doi:10.30574/wjbphs.2023.16.2.0467 · Cited by 2
The Transformative Role of Artificial Intelligence in Training Obstetrics and Gynecology Residents
Choudhary, A., & Choudhary, A. N. (2023) · Journal of Obstetric and Gynaecological Practices POGS · doi:10.5005/jogyp-11012-0012 · Cited by 2
Artificial Intelligence in Medical Imaging: An Overview
Seeram, E. (2023) · Springer eBooks · doi:10.1007/978-3-031-46266-5_9 · Cited by 2
Mudenda, S., & Mohamed, S. (2024) · Scholars Academic Journal of Biosciences · doi:10.36347/sajb.2024.v12i01.003 · Cited by 2
Introduction to machine learning and artificial intelligence
Mustapha, M. T., Özşahin, İ., & Ozsahin, D. U. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95462-4.00001-7 · Cited by 2
Review of the potential benefits and challenges of artificial intelligence in clinical laboratory
Tiwade, Y. R., Bankar, N. J., Mishra, V. H., & Sajjanar, A. (2024) · Journal of Cellular Biotechnology · doi:10.3233/jcb-230119 · Cited by 2
Identifying Parkinson's Patients by a Functional Gradient Boosting Approach
Sharma, S., Gupta, M., Goyal, K., Goyal, M., & Sharma, P. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-1115-8.ch017 · Cited by 2
Desai, A. B., Gangodkar, D. R., Pant, K., & Pant, B. (2024) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence60223.2024.10463450 · Cited by 2
Hotmangatur, A. P., & Bachtiar, A. (2024) · Contagion Scientific Periodical Journal of Public Health and Coastal Health · doi:10.30829/contagion.v6i1.19198 · Cited by 3
Shukla, S., Brandwein‐Weber, M., Samankan, S., Ayad, A., Rabie, M., & Doyle, S. T. (2024) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.3006890 · Cited by 2
Medical applications of artificial intelligence
Pradhan, B. K., Biswas, D., Neelapu, B. C., Sivaraman, J., & Pal, K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-19073-5.00012-4 · Cited by 2
Dabla, P. K., Upreti, K., Shrivastav, D., Mehta, V., & Singh, D. (2024) · World Journal of Methodology · doi:10.5662/wjm.v14.i2.92608 · Cited by 2
Artificial Intelligence in Health Care: Various Applications
Protrka, N., & Abazi, B. (2024) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro60963.2024.10569971 · Cited by 3
“Explainable AI” Disease Detection with Reasoning
Kumar, M., Ramrakhiyani, K., & Garg, H. (2024) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic60222.2024.10575153 · Cited by 2
Haule, H., Piper, I., Jones, P. A., Lo, T.-Y. M., & Escudero, J. (2024) · IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) · doi:10.1109/icasspw62465.2024.10627040 · Cited by 2
Optimizing Perimenopausal Care
Mishra, P. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-2342-7.ch010 · Cited by 2
Analysis of Global Research Trends and Development Trends in Smart Healthcare: A Bibliometric Study
Yang, S., & Zhu, J. (2024) · International Symposium on Robotics, Artificial Intelligence and Information Engineering (RAIIE) · doi:10.1145/3689299.3689315 · Cited by 2
MedSeg: A Medical Image Analyzer
Mehta, S., Pancholi, K., Pandey, S., & Dhage, S. (2024) · Control Instrumentation System Conference (CISCON) · doi:10.1109/ciscon62171.2024.10696156 · Cited by 2
Ma, L., & Fan, H. (2025) · American Journal of Tropical Medicine and Hygiene · doi:10.4269/ajtmh.24-0248 · Cited by 2
AI enhanced healthcare: Opportunities, challenges, ethical considerations, and future risk
Gupta, S., Mohanty, S., Behera, D. K., & Nayak, S. R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-24788-0.00006-6 · Cited by 2
Potential Risks and Ethical Issues with Intelligent Technologies in Orthopedics
Toyooka, S., Martin, R. K., Engebretsen, L., & Moatshe, G. (2024) · Springer eBooks · doi:10.1007/978-3-031-70310-2_20 · Cited by 2
Managing Dyslipidemia in Children: Current Approaches and the Potential of Artificial Intelligence.
Zaheer, A., Saeed, H., Arshad, M. K., Iftikhar, U., Abid, A., Khan, M. H., Khan, A., & Akbar, A. (2024) · Cardiology in Review · doi:10.1097/crd.0000000000000816 · Cited by 2
A Review on Medical Image Segmentation Using Deep Learning
Sherifa, C. A., & Kanisha, B. (2024) · Algorithms for intelligent systems · doi:10.1007/978-981-97-5791-6_6 · Cited by 2
A Hybrid EfficientNetB0-XGBoost Framework for Efficient Brain Tumor Classification Using MRI Images
Kumar, H., Pandey, D., Yadav, A., Agarwal, R., Tripathi, S., & Malhotra, S. K. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1032-9.ch033 · Cited by 2
AI in diagnostics: Enhancing accuracy and efficiency
Khan, S. M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33584-6.00013-x · Cited by 2
Lubanga, A. F., Kafera, G., Bwanali, A. N., Choi, Y., Lee, C., Ham, E., Lee, J. Y., Chung, J., & Chung, J. (2025) · Frontiers in Oral Health · doi:10.3389/froh.2025.1443313 · Cited by 2
Wahyudi, I., Utomo, C. P., Djauzi, S., Fathurahman, M., Situmorang, G. R., Rodjani, A., Raharja, P. A. R., Yonathan, K., Santoso, B., Khresna, D., et al. (2025) · Journal of Pediatric Surgery · doi:10.1016/j.jpedsurg.2025.162358 · Cited by 2
Artificial Intelligence in Health and Well-Being
Thomas, T. S. P., & Leon, L. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-0659-9.ch003 · Cited by 2
Naidoo, C. M., Obi, C. L., & Mkolo, N. M. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9301-7.ch006 · Cited by 5
Evaluating ChatGPT for Disease Prediction: A Comparative Study on Heart Disease and Diabetes
Alomari, E. A. (2025) · BioMedInformatics · doi:10.3390/biomedinformatics5030033 · Cited by 2
Metadata
- Authors
- Kumar, Y., Koul, A., Singla, R., & Ijaz, M. F.
- Affiliations
- Indus University (IN)
- Shri Mata Vaishno Devi University (IN)
- Sejong University (KR)
- Corresponding author
- Kumar, Yogesh
- Koul, Apeksha
- Singla, Ruchi
- Ijaz, Muhammad Fazal
- Venue
- Journal of Ambient Intelligence and Humanized Computing
- Published
- 2022-01-13
- Volume
- 14
- Issue
- 7
- Pages
- 8459-8486
- ISSN
- 1868-5137 (ISSN-L)
- 1868-5145
- PMC
- PMC8754556
- OpenAlex
- W4205941964
- Cited by
- 1,040
- References
- 143
- Citations by year
- 2026: 136 · 2025: 322 · 2024: 332 · 2023: 200 · 2022: 48
- FWCI
- 32.90
- Type
- journal article
- Language
- en
- Status
- Retracted
- Update notices
- retraction (2026-03-09)
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Ambient Intelligence and Humanized Computing (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- sejong university (Research fund)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- artificial intelligence in healthcare, disease diagnosis, diagnostic imaging, deep learning, machine learning, medical image classification, feature extraction, early disease prediction, Alzheimer's disease, cancer diagnosis, diabetes mellitus, hypertension, skin diseases, liver disease, tuberculosis, stroke, chronic heart disease, model performance comparison
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
