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RETRACTED ARTICLE: Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda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 301–400 of 999
Trustworthy Artificial Intelligence in the Context of Metrology
Adel, T., Bilson, S., Mark Levene, A., & Thompson, A. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-55817-7_4 · Cited by 11
AI-driven microbial medicine development
Dharmani, K., Sinha, A., & Chaturvedi, N. (2024) · Methods in microbiology · doi:10.1016/bs.mim.2024.05.008 · Cited by 11
Current and emerging technologies to develop Point-of-Care Diagnostics in medical mycology
Matsuo, T., Wurster, S., Hoenigl, M., & Kontoyiannis, D. P. (2024) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2024.2397515 · Cited by 12
Sreedharan, J. K., Saleh, F., Alqahtani, A., Albalawi, I. A. J., Gopalakrishnan, G. K., Alahmed, H. A., Alsultan, B. A., Alalharith, D. M., Alnasser, M., Alahmari, A. D., et al. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1422551 · Cited by 11
AI frontiers in emergency care: the next evolution of nursing interventions
Mani, Z. A., & Albagawi, B. S. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1439412 · Cited by 13
Chaitanya, M. K., Sharma, L. D., Rahul, J., Sharma, D., & Roy, A. (2023) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/acbd53 · Cited by 12
The Evolving Landscape of Artificial Intelligence Applications in Animal Health
Min, P. K., Mito, K., & Kim, T. H. (2024) · Indian Journal of Animal Research · doi:10.18805/ijar.bf-1742 · Cited by 27
A Competency Framework for Medical AI Education: Mixed Methods Study
Cai, C., Zhu, G., Zhou, S., Ng, O., Duell, J., Ho, W. K., Chen, D. M., Lee, B. T. K., Li, F., Liu, S., et al. (2026) · JMIR Medical Education · doi:10.2196/91116 · Cited by 9
Transformative potential of artificial intelligence on health care and research in Africa
Bockarie, M. J., Ansumana, R., Machingaidze, S., de Souza, D. K., Fatoma, P., Zumla, A. I., & Lee, S. S. (2024) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2024.107011 · Cited by 10
Deep Learning Diagnostic Classification of Cervical Images to Augment Colposcopic Impression
Aquilina, A., & Papagiannakis, E. (2024) · Journal of Lower Genital Tract Disease · doi:10.1097/lgt.0000000000000815 · Cited by 12
Robustness and Reliability Testing in Healthcare Using Artificial Intelligence
Khinvasara, T., Shankar, A., & Wong, C. (2024) · Asian Journal of Research in Computer Science · doi:10.9734/ajrcos/2024/v17i7482 · Cited by 11
Voice assistants’ influence on loneliness in older adults: a systematic review
Martínez, E. C., Encuentra, E. H., & Pousada, M. (2024) · Disability and Rehabilitation Assistive Technology · doi:10.1080/17483107.2024.2397030 · Cited by 12
ViT‐AMD: A New Deep Learning Model for Age‐Related Macular Degeneration Diagnosis From Fundus Images
Le, N. T., Truong, T. L., Deelertpaiboon, S., Srisiri, W., Pongsachareonnont, P. F., Suwajanakorn, D., Mavichak, A., Itthipanichpong, R., Asdornwised, W., Benjapolakul, W., et al. (2024) · International Journal of Intelligent Systems · doi:10.1155/2024/3026500 · Cited by 10
Masawi, T. J., Miller, E. I., Rees, D. J., & Thomas, R. A. (2025) · Journal of Decision System · doi:10.1080/12460125.2025.2458874 · Cited by 15
Diabetes diagnosis using a hybrid CNN LSTM MLP ensemble
Fan, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-12151-y · Cited by 10
Accurate Prediction of Anxiety Levels in Asian Countries Using a Fuzzy Expert System
Ramzan, M., Hamid, M., Alhussan, A. A., AlEisa, H. N., & Abdallah, H. A. (2023) · Healthcare · doi:10.3390/healthcare11111594 · Cited by 15
Chinnaiyan, K., Mugundhan, S. L., Narayanasamy, D., & Mothilal, M. (2024) · Current Drug Therapy · doi:10.2174/0115748855313948240711043701 · Cited by 11
Hubert, K. F., Awa, K. N., & Zabelina, D. L. (2023) · doi:10.31234/osf.io/6py2m · Cited by 10
Enhancing AI-CDSS with U-AnoGAN: Tackling data imbalance
Mun, C., Ha, H., Lee, O., & Cheon, M. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107954 · Cited by 11
Cihan, P. (2024) · Kafkas Universitesi Veteriner Fakultesi Dergisi · doi:10.9775/kvfd.2023.30908 · Cited by 9
Droppelmann, G., Rodríguez, C., Jorquera, C., & Feijoo, F. (2024) · EFORT Open Reviews · doi:10.1530/eor-23-0174 · Cited by 11
AI in autoimmune diseases: Transforming diagnosis and treatment
Mane, D. V., Deshmukh, A. M., Ambare, R. H., Solankar, A. A., & Madane, C. S. (2025) · Journal of Pharmaceutical and Biological Sciences · doi:10.18231/j.jpbs.2024.017 · Cited by 10
Pharmacological Approaches and Innovative Strategies for Individualized Patient Care
Verma, A. K., Singh, K., Gupta, J. K., Kumar, S., & Jain, D. (2025) · Recent Patents on Biotechnology · doi:10.2174/0118722083359334250116063638 · Cited by 11
Music genre classification using deep neural networks and data augmentation
Thành, C. B., Thủy, Đ. T., & Van, L. T. (2025) · Entertainment Computing · doi:10.1016/j.entcom.2025.100929 · Cited by 15
Cutting-edge deep-learning based tools for metagenomic research
Karin, E. L., & Steinegger, M. (2025) · National Science Review · doi:10.1093/nsr/nwaf056 · Cited by 10
AI and IoT Integration for Chronic Disease Management in Smart Healthcare
Tamhankar, G., Gulhane, M., Ghorpade, V. V., Sheikh, F., Rakesh, N., & Kashyap, K. (2025) · International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3) · doi:10.1109/isac364032.2025.11156668 · Cited by 9
AI-Powered Synergistic Robotics and IoT Ecosystems for Predictive and Preventive Healthcare
Ravikumar, R. N., & Aarthi, S. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-5447-7.ch001 · Cited by 9
Krishnamoorthy, U. (2025) · Analytical Methods · doi:10.1039/d5ay01580d · Cited by 9
Saseendran, A., & Thomas, M. V. (2025) · STEM Education · doi:10.3934/steme.2025046 · Cited by 22
Tawhid, M. N. A., Siuly, S., Wang, K., & Wang, H. (2022) · PLoS ONE · doi:10.1371/journal.pone.0277555 · Cited by 9
Prediction of cyanotic and acyanotic congenital heart disease using machine learning models
Shahid, S., Khurram, H., Lim, A., Shabbir, M. F., & Billah, B. (2024) · World Journal of Clinical Pediatrics · doi:10.5409/wjcp.v13.i4.98472 · Cited by 10
Iannone, S., Kaur, A., & Johnson, K. B. (2026) · Journal of General Internal Medicine · doi:10.1007/s11606-025-09938-0 · Cited by 8
Deep Neural Networks and Applications in Medical Research
Abut, S., Okut, H., Zackula, R., & Kallail, K. J. (2023) · Artificial intelligence · doi:10.5772/intechopen.112371 · Cited by 10
Nakamae, Y., Deguchi, R., Nemoto, M., Kimura, Y., Yamashita, S., Kohjimoto, Y., & Hara, I. (2023) · Urolithiasis · doi:10.1007/s00240-023-01506-7 · Cited by 8
Deloitte Consulting, L. L. P., & Hampiholi, N. (2023) · International Journal of Engineering Applied Sciences and Technology · doi:10.33564/ijeast.2023.v08i05.005 · Cited by 8
Bhabad, S., Lamkhade, D., Koyate, S., Karanjkhele, K., Kale, V., & Doke, R. R. (2023) · IP International Journal of Comprehensive and Advanced Pharmacology · doi:10.18231/j.ijcaap.2023.034 · Cited by 8
Artificial Intelligence for Infectious Disease Detection: Prospects and Challenges
Siddique, M. M., Seraj, M. M. B., Adnan, M. N., & Galib, S. M. (2024) · Springer eBooks · doi:10.1007/978-3-031-59967-5_1 · Cited by 8
An Improved Grey Wolf Optimization Algorithm for Heart Disease Prediction
Niu, S., Zhou, Y., Li, Z., Huang, S., & Zhou, Y. (2024) · Smart innovation, systems and technologies · doi:10.1007/978-981-97-3210-4_43 · Cited by 8
Bedia, A. S., Mulla, S. A., Patil, A., Bedia, S. V., Ghadage, M., & Mali, S. D. (2024) · Cureus · doi:10.7759/cureus.66836 · Cited by 8
Fan, K. S., & Fan, K. H. (2024) · Dermato · doi:10.3390/dermato4040013 · Cited by 9
Diabetic Wound Healing: Factors, Mechanisms, and Treatment Strategies Using Herbal Components
Porwal, S., Malviya, R., Sundram, S., Sridhar, S. B., & Shareef, J. (2025) · Current Drug Targets · doi:10.2174/0113894501354898241220075327 · Cited by 8
Building Trustworthy AI systems for secure digital health services
Alahmed, Y., & Mazroub, M. (2024) · International Arab Conference on Information Technology (ACIT) · doi:10.1109/acit62805.2024.10876871 · Cited by 8
Artificial intelligence for digital healthcare in the low and medium income countries
Sibiya, S. S., Hurchund, R., Omondi, B. O., & Owira, P. M. O. (2025) · Health and Technology · doi:10.1007/s12553-025-00950-2 · Cited by 10
Kuppanda, P. M., Janda, M., Soyer, H. P., & Caffery, L. J. (2025) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2025.01.013 · Cited by 9
Cascading failure prediction and recovery in large-scale critical infrastructure networks: A survey
Li, B., Hu, W., Yuan, C., Wang, X., Li, Y., & Wu, Y. (2025) · Information and Software Technology · doi:10.1016/j.infsof.2025.107705 · Cited by 8
Annan, R., & Qingge, L. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100751 · Cited by 8
Razavi, J. K., Dhafar, H. O., & BaHammam, A. S. (2025) · Saudi Journal for Health Sciences · doi:10.4103/sjhs.sjhs_26_25 · Cited by 9
Lederman, O., Llana, A., Murray, J., Stanton, R., Chugh, R., Haywood, D., Burdett, A., Warman, G., Walker, J., & Hart, N. H. (2025) · BMC Sports Science Medicine and Rehabilitation · doi:10.1186/s13102-025-01182-7 · Cited by 9
Khalil, C. A., Saab, A., Rahme, J., Jacques Bouaud, A., & Brigitte Séroussi, A. (2025) · BMC Nursing · doi:10.1186/s12912-025-03272-w · Cited by 8
Artificial Intelligence-Powered Chronic Obstructive Pulmonary Disease Detection Techniques—A Review
Sait, A. R. W., & Shaikh, M. A. (2025) · Diagnostics · doi:10.3390/diagnostics15202562 · Cited by 8
Dehbaghi, H. A., & Khoshgard, K. (2025) · International Journal of Emergency Medicine · doi:10.1186/s12245-025-01049-1 · Cited by 16
Zaheen, U., Rakha, A., Hassan, Q., Khan, M. F., Akhtar, S., & Munawar, A. (2025) · Journal of Forensic and Legal Medicine · doi:10.1016/j.jflm.2025.103043 · Cited by 8
Rizzo, M., Veneri, A., Marcuzzo, M., Zangari, A., Albarelli, A., Lucchese, C., Nobile, M. S., & Conati, C. (2025) · BMC Medical Research Methodology · doi:10.1186/s12874-025-02703-1 · Cited by 12
Minciuna, C.-E., Tanase, M., Manuc, T. E., Tudor, Ș., Herlea, V., Dragomir, M. P., Călin, G. A., & Vasilescu, C. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.09.010 · Cited by 8
Machine and Deep Learning Techniques to Classify and Predict Thyroid Diseases
Gosain, M., Gupta, S., & Kaur, S. (2022) · International Conference on Intelligent Engineering and Management (ICIEM) · doi:10.1109/iciem54221.2022.9853067 · Cited by 8
Expending the power of artificial intelligence in preclinical research: an overview
Diaconu, A.-Ș., Cojocaru, F.-D., Gardikiotis, I., Agrigoroaie, L., Furcea, D. M., Pasat, A. S., Suciu, G. D., Rezuș, C., & Dodi, G. (2022) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1254/1/012036 · Cited by 9
Thakur, K., Kaur, M., & Kumar, Y. (2022) · International Conference on Technological Advancements in Computational Sciences (ICTACS) · doi:10.1109/ictacs56270.2022.9988282 · Cited by 8
Self-citationBed Allocation Optimization Based on Survival Analysis, Treatment Trajectory and Costs Estimations
Karboub, K., & Tabaa, M. (2023) · IEEE Access · doi:10.1109/access.2023.3260184 · Cited by 3
Shi, H., Shen, Y., & Li, L. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1221602 · Cited by 8
Machine learning in healthcare strategic management: a systematic literature review
Salhout, S. M. (2023) · Arab Gulf Journal of Scientific Research · doi:10.1108/agjsr-06-2023-0252 · Cited by 8
Sultana, A., Akhtar, F., Heyat, M. B. B., Singh, P., Parveen, S., Kumar, S., Sayeed, E., Akhtar, S., Singh, B., Rahmani, K., et al. (2023) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip60502.2023.10385245 · Cited by 8
Gong, A., Fu, W., Li, H., Guo, N., & Pan, T. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1364519 · Cited by 8
Predicting mortality of cancer patients using artificial intelligence, patient data and blood tests
Martins, T. D., Maciel-Filho, R., Montalvão, S. A. D. L., Gois, G. D. S. S., Bannoud, M. A., Ottaiano, G. Y., Anhaia, T. R. A., Almeida, M. E. A., Ferreira, M. R. M., Martinelli, B. M., et al. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09915-4 · Cited by 8
Menekşeoğlu, A. K., & İş, E. E. (2024) · Revista da Associação Médica Brasileira · doi:10.1590/1806-9282.20240241 · Cited by 8
de Arriba-Pérez, F., & García-Méndez, S. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-024-09508-2 · Cited by 8
Kabdushev, S. B., Gabrielyan, O., Копишев, Э. Е., & Suleimenov, I. E. (2025) · Royal Society Open Science · doi:10.1098/rsos.242149 · Cited by 9
Enhancing cardiac disease detection via a fusion of machine learning and medical imaging
Tao, Y., & Chen, K. (2025) · Scientific Reports · doi:10.1038/s41598-025-12030-6 · Cited by 13
Kumpatla, G., Veresi, H., Bala Subrahmanyam, P., Abhishek, S., & Anjali, T. (2023) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac58438.2023.10290627 · Cited by 8
Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057400 · Cited by 10
The Future of Cystic Fibrosis Care: Exploring AI's Impact on Detection and Therapy
Basu, B., Dutta, S., Rahaman, M., Bose, A., Das, S., Prajapati, J. B., & Prajapati, B. (2024) · Current Respiratory Medicine Reviews · doi:10.2174/011573398x283365240208195944 · Cited by 7
Inteligência artificial em saúde
Amaro, E., Nakaya, H. I., & Rizzo, L. V. (2024) · Revista USP · doi:10.11606/issn.2316-9036.i141p41-50 · Cited by 7
State-of-the-art skin disease classification: a review of deep learning models
Jaiyeoba, O., Ogbuju, E., Ataguba, G., Jaiyeoba, O., Omaye, J. D., Eze, I. O., & Oladipo, F. O. (2025) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-024-00495-w · Cited by 10
Automated Early Prediction of Parkinson’s Disease Based on Artificial Intelligent Techniques
Bakry, S. A., & Mahmoud, N. M. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-025-10358-9 · Cited by 7
Li, Y., Ding, X. X., Chen, Y., Li, Y., & Ma, N. (2025) · Designing Interactive Systems Conference · doi:10.1145/3715336.3735795 · Cited by 9
AI-Y: An AI Checklist for Population Ethics Across the Global Context
Hswen, Y., Naslund, J. A., Hurley, M., Ragon, B., Handley, M. A., Fang, F., Haroz, E. E., Nabende, J. N., van Heerden, A. C., & Nsoesie, E. O. (2025) · Current Epidemiology Reports · doi:10.1007/s40471-025-00362-w · Cited by 8
Advancing AI Literacy in Medical Education: A Medical AI Competency Framework Development
Cai, C., Duell, J., Chen, D. M., Ho, W. K., Lee, B. T. K., Li, F., Liu, S., Ng, O., Sudarshan, V. K., Zhou, S., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98462-4_15 · Cited by 16
How public involvement can improve the science of AI
Matias, J. N., & Price, M. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2421111122 · Cited by 13
John, A. S., & Kavic, S. M. (2022) · Artificial Intelligence Surgery · doi:10.20517/ais.2022.24 · Cited by 7
Chen, W., Xia, H., Song, R., & Pan, C. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/ace63e · Cited by 7
A Deep Learning Based Breast Cancer Classification System Using Mammograms
Meenalochini, G., & Ramkumar, S. (2023) · Journal of Electrical Engineering and Technology · doi:10.1007/s42835-023-01747-x · Cited by 9
Vaishya, R., Gupta, B. M., Mamdapur, G. M. N., Vaish, A., Bhadani, J. S., & Mukhopadhaya, J. (2024) · Indian Journal of Orthopaedics · doi:10.1007/s43465-024-01176-6 · Cited by 7
Sheikh, B. U. H. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111909 · Cited by 9
Durmuş, M. A., Kömeç, S., & Gülmez, A. (2024) · Immunologic Research · doi:10.1007/s12026-024-09527-z · Cited by 7
Yuriy, R., Тatarina, O., Kaminskyy, V., Silina, T. M., & Bashkirova, L. (2024) · Futurity Medicine. · doi:10.57125/fem.2024.12.30.02 · Cited by 11
Chaithanyadas, K. V., & King, D. M. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107413 · Cited by 7
Is AI really improving the quality of digital diagnosis
Sharma, D. K., Yede, K., Rahate, V., Mollay, M. H., Anawade, P. A., & Gahane, S. (2024) · DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIHEI) · doi:10.1109/idicaiei61867.2024.10842919 · Cited by 7
Artificial intelligence changing the future of healthcare diagnostics
Mishra, V. H., Ugemuge, S., & Tiwade, Y. R. (2023) · Journal of Cellular Biotechnology · doi:10.3233/jcb-230118 · Cited by 9
Dadjouy, S., & Sajedi, H. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106149 · Cited by 7
Ravikumar, A., & Sriraman, H. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.035848 · Cited by 7
Ting, D. S. W., Ong, J. C. L., Jin, L., Elangovan, K., Lim, G. Y. S., Lim, D. Y. Z., Sng, G. G. R., Ke, Y., Tung, J. Y. M., Zhong, R. J., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-4023142/v1 · Cited by 7
Exploring Machine Learning Methods for Developing a Predictive System for Parkinson's Disease
Das, S., Saha, T., Nath, I., & Mondal, D. (2024) · Biosciences Biotechnology Research Asia · doi:10.13005/bbra/3248 · Cited by 7
A Wide Application of Artificial Intelligence in Pharma Field
-, V. S. H., & Baitule, A. W. (2024) · Asian Journal of Pharmaceutical Research · doi:10.52711/2231-5691.2024.00064 · Cited by 6
Harnessing machine learning for improved diagnosis, drug discovery, and patient care
Paul, J. K., Azmal, M., Talukder, O. F., Haque, A. S. N. B., Meem, M., & Ghosh, A. (2025) · Computational and Structural Biotechnology Reports · doi:10.1016/j.csbr.2025.100051 · Cited by 8
Ultrasound Diagnosis of Pelvic Organ Prolapse Using Artificial Intelligence
García‐Mejido, J. A., Galán‐Paez, J., Solis‐Martín, D., Palacín, F. F., Fernández-Palacín, A., & Sainz‐Bueno, J. A. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14113634 · Cited by 8
Revathi Unni, K., Amrisa Pavithra, E., Subramanian, R., & Ks, S. (2025) · Current Treatment Options in Oncology · doi:10.1007/s11864-025-01331-7 · Cited by 6
Behavioral Impacts of AI Reliance in Diagnostics: Balancing Automation with Skill Retention
Sunday, O. (2025) · Epidemiology and Health Data Insights · doi:10.63946/ehdi/16894 · Cited by 6
Schäfer, U., Sipos, L., & Müller-Birn, C. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757451 · Cited by 10
Narasimharao, M., Swain, B., Priyadarshi, R., Nayak, P. P., & Bhuyan, S. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10450-1 · Cited by 7
Deepanshu, D., Singh, K., & Dhawan, S. (2022) · International Conference on Communication, Computing and Internet of Things (IC3IoT) · doi:10.1109/ic3iot53935.2022.9767930 · Cited by 6
Exploring the Benefits and Challenges of Artificial Intelligence (AI) in Nursing
Romero, C. B., Opoku, O. A., Syabariyah, S., Astuti, D. A., Asif, I., & Rostinah (2023) · Engineering Science Letter · doi:10.56741/esl.v2i01.278 · Cited by 6
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
