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RETRACTED ARTICLE: Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda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 201–300 of 999
Mitigating bias in clinical machine learning models
Perez-Downes, J. C., Tseng, A. S., McConn, K. A., Elattar, S. M., Sokumbi, O., Sebro, R. A., Allyse, M. A., Dangott, B. J., Carter, R. E., & Adedinsewo, D. (2024) · Current Treatment Options in Cardiovascular Medicine · doi:10.1007/s11936-023-01032-0 · Cited by 23
Digital Transformation in Healthcare and Medical Practices
Martín, M. S., & Urbistondo, P. A. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-1214-8.ch009 · Cited by 24
Integration of Multi-Source Medical Data for Medical Diagnosis Question Answering
Peng, Q., Cai, Y., Liu, J., Zou, Q., Chen, X., Zhong, Z., Wang, Z., Xie, J., & Li, Q. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3496862 · Cited by 23
Wissel, B. D., Greiner, H. M., Glauser, T. A., Mangano, F. T., Holland‐Bouley, K. D., Zhang, N., Szczesniak, R. D., Santel, D., Pestian, J. P., & Dexheimer, J. W. (2023) · Epilepsia · doi:10.1111/epi.17629 · Cited by 22
Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis
Perkins, M., & Roe, J. (2023) · F1000Research · doi:10.12688/f1000research.142411.1 · Cited by 120
Survey on medical image encryption: From classical to deep learning-based approaches
Prasad, S., & Singh, A. K. (2025) · Computers and Electrical Engineering · doi:10.1016/j.compeleceng.2024.110011 · Cited by 21
Artificial Intelligence on Diagnostic Aid of Leprosy: A Systematic Literature Review
Fernandes, J. R. N., Teles, A. S., Fernandes, T. R. S., Lima, L. D. B., Balhara, S., Gupta, N., & Teixeira, S. S. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm13010180 · Cited by 23
Melker, S., Gabrils, E., Villavicencio, V., Faraon, M., & Rönkkö, K. (2025) · International Journal of Technology and Design Education · doi:10.1007/s10798-025-09964-3 · Cited by 38
Ngcobo, S., Madela-Mntla, E. N., Shock, J. P., Louw, M., Mbonambi, L., Serite, T., & Rossouw, T. M. (2025) · Journal of the International AIDS Society · doi:10.1002/jia2.70045 · Cited by 28
Hamraz, M., Ali, A., Mashwani, W. K., Aldahmani, S., & Khan, Z. (2023) · PLoS ONE · doi:10.1371/journal.pone.0284619 · Cited by 21
Batool, Z., Kamal, M. A., & Shen, B. (2024) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-024-05903-2 · Cited by 21
Navigating the future of health care with AI-driven digital therapeutics
Vasdev, N., Gupta, T., Pawar, B., Bain, A., & Tekade, R. K. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.104110 · Cited by 25
Moghadam, M. P., Moghadam, Z. A., Qazani, M. R. C., Pławiak, P., & Alizadehsani, R. (2024) · IEEE Access · doi:10.1109/access.2024.3450970 · Cited by 22
Khanagar, S. B., Alfouzan, K., Alkadi, L., Albalawi, F., Iyer, K., & Awawdeh, M. A. (2022) · Applied Sciences · doi:10.3390/app12199819 · Cited by 19
Giraldo‐Roldán, D., Ribeiro, E. C. C., Araújo, A. L. D., Penafort, P. V. M., da Silva, V. M., Câmara, J., Pontes, H. A. R., Martins, M. D., Oliveira, M. C., Santos‐Silva, A. R., et al. (2023) · Journal of Oral Pathology and Medicine · doi:10.1111/jop.13481 · Cited by 20
Learned prediction of cholesterol and glucose using ARIMA and LSTM models – A comparison
Krishnamoorthy, U., Karthika, V., Mathumitha, M. K., Panchal, H. N., Jatti, V. K. S., & Kumar, A. (2023) · Results in Control and Optimization · doi:10.1016/j.rico.2023.100362 · Cited by 20
Nanoinformatics and nanotechnology in anti-inflammatory therapy: A review
Mishra, A. K., Rani, L., Singh, R., Dewangan, H. K., Sahoo, P. K., & Kumar, V. (2024) · Journal of Drug Delivery Science and Technology · doi:10.1016/j.jddst.2024.105446 · Cited by 20
Decoding myasthenia gravis: advanced diagnosis with infrared spectroscopy and machine learning
Severcan, F., Ozyurt, I., Doğan, A., Severcan, M., Gurbanov, R., KÜÇÜKCANKURT, F., Elibol, B., Tiftikçioğlu, B. İ., Gürsoy, E. B., Yangin, M. N., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-66501-3 · Cited by 20
Artificial intelligence and omics in malignant gliomas
Tambi, R., Zehra, B., Vijayakumar, A., Satsangi, D., Uddin, M. J., & Berdiev, B. K. (2024) · Physiological Genomics · doi:10.1152/physiolgenomics.00011.2024 · Cited by 22
Artificial Intelligence Diagnosing of Oral Lichen Planus: A Comparative Study
Yu, S., Sun, W., Mi, D., Jin, S., Wu, X. F., Xin, B., Zhang, H., Wang, Y., Sun, X., & He, X. J. (2024) · Bioengineering · doi:10.3390/bioengineering11111159 · Cited by 19
Kumar, R., Singh, A. K., Kassar, A. S. A., Humaida, M. I., Joshi, S., & Sharma, M. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.059152 · Cited by 32
Application of artificial intelligence in electrochemical diagnostics for human health
Ranjan, K., Barar, B., Prasad, M., & Prasad, G. (2025) · Discover Electrochemistry. · doi:10.1007/s44373-025-00042-w · Cited by 21
Advances in artificial intelligence (AI)-based diagnosis in clinical practice—correspondence
Chakraborty, S., Chopra, H., Akash, S., Chakraborty, C., & Dhama, K. (2023) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000000959 · Cited by 19
Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects
Elshoeibi, A. M., Badr, A. A., Elsayed, B., Metwally, O. H., Elshoeibi, R. M., Elhadary, M. R., Elshoeibi, A. M., Attya, M. A., Khadadah, F. M., Alshurafa, A., et al. (2023) · Cancers · doi:10.3390/cancers16010065 · Cited by 19
Nicora, G., Catalano, M., Bortolotto, C., Achilli, M. F., Messana, G., Tito, A. L., Consonni, A., Cutti, S., Comotto, F., Stella, G. M., et al. (2024) · Journal of Imaging · doi:10.3390/jimaging10050117 · Cited by 19
Research on ChatGPT in tourism and hospitality: A systematic review and future directions
Quang, T. D., Vo‐Thanh, T., Nghi, N. H., Phuong, B. T., & Bui, T. T. B. (2025) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2025.104287 · Cited by 22
Alghamdi, S. A., & Alashban, Y. I. (2023) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2023.100569 · Cited by 18
Khan, M. A., Iqbal, N., Jamil, H., Qayyum, F., Jang, J., Khan, S., Kim, J., & Kim, D. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.11.036 · Cited by 20
A personalized probabilistic approach to ovarian cancer diagnostics
Ban, D., Housley, S. N., Matyunina, L. V., McDonald, L. D., Bae-Jump, V. L., Benigno, B. B., Skolnick, J., & McDonald, J. F. (2024) · Gynecologic Oncology · doi:10.1016/j.ygyno.2023.12.030 · Cited by 21
Shukur, B. S., Ghani, M. K. A., & Aboobaider, B. M. (2024) · Mesopotamian Journal of Artificial Intelligence in Healthcare · doi:10.58496/mjaih/2024/005 · Cited by 19
GPT-4/4V's performance on the Japanese National Medical Licensing Examination
Kawahara, T., & Sumi, Y. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2342545 · Cited by 18
Sha, Y., Zhang, Q., Zhai, X., Hou, M., Lu, J., Meng, W., Wang, Y., Li, K., & Ma, J. (2024) · iScience · doi:10.1016/j.isci.2024.111313 · Cited by 18
Termine, A., Fabrizio, C., Caltagirone, C., Petrosini, L., & Initiative, O. B. O. T. F. L. D. N. (2022) · Life · doi:10.3390/life12070947 · Cited by 21
Chaki, J., & Deshpande, G. (2024) · Current Neuropharmacology · doi:10.2174/1570159x22999240531160344 · Cited by 19
Leenen, J. P. L., Hiemstra, P., Ten Hoeve, M. M., Jansen, A. C. J., van Dijk, J. D., Vendel, B., Versteeg, G., Hakvoort, G. A., & Hettinga, M. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000847 · Cited by 17
Digital transformation in pharmaceuticals: The impact of AI on supply chain management
Vadaga, A. K., Dokuburra, U. R., Nekkanti, H., Gudla, S. S., & Kumari, R. (2025) · Intelligent Hospital · doi:10.1016/j.inhs.2025.100008 · Cited by 21
Kim, S., An, H., Cho, H., Min, K., Hong, J. H., Lee, S., Song, J. Y., Lee, J. K., & Lee, N. W. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12124024 · Cited by 17
The Role of Virtual Triage in Improving Clinician Experience and Satisfaction: A Narrative Review
Gellert, G. A., Rasławska-Socha, J., Marcjasz, N., Price, T. J., Heyduk, A., Młodawska, A., Kuszczyński, K., Jędruch, A., & Orzechowski, P. D. (2023) · Telemedicine Reports · doi:10.1089/tmr.2023.0020 · Cited by 24
Liu, Y., Feng, Y., Qian, L., Wang, Z., & Hu, X. (2023) · Experimental Biology and Medicine · doi:10.1177/15353702231220664 · Cited by 18
Exploring Interpretable AI Methods for ECG Data Classification
Ojha, J., Haugerud, H., Yazidi, A., & Lind, P. G. (2024) · doi:10.1145/3643488.3660294 · Cited by 22
Shumeiko, N., & Osadcha, K. (2024) · Journal of Physics, Conference Series · doi:10.1088/1742-6596/2871/1/012027 · Cited by 14
Rare Disease Policy in High-Income Countries: An Overview of Achievements, Challenges, and Solutions
Gentilini, A., Neez, E., & Wong‐Rieger, D. (2025) · Value in Health · doi:10.1016/j.jval.2024.12.009 · Cited by 17
Liu, L.-R., Hung-Wen, C., Huang, M., Huang, S.-T., Tsai, M., Chang, C., & Chang, K.-S. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.893208 · Cited by 19
Malik, S., Patro, S. G. K., Mahanty, C., Lasisi, A., Naveed, Q. N., Buradi, A., Emma, A. F., Kumar, S., & Mubarakali, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-14793-4 · Cited by 16
Sebti, M. R., McCarthy, U., Ktenioudaki, A., Russo, M., & Merenda, M. (2026) · Sensors · doi:10.3390/s26051685 · Cited by 16
Rey, R., Gualtieri, R., Scala, G. C. L., & Posfay-Barbe, K. M. (2024) · European Journal of Pediatric Surgery · doi:10.1055/a-2257-5122 · Cited by 16
The Nexus of Healthcare and Technology
Darda, P., & Matta, N. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-3661-8.ch013 · Cited by 17
Approaches for the Use of AI in Workplace Health Promotion and Prevention: Systematic Scoping Review
Lange, M., Löwe, A., Kayser, I., & Schaller, A. (2024) · JMIR AI · doi:10.2196/53506 · Cited by 20
An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique
Elhassan, S. M., Darwish, S. M., & Elkaffas, S. M. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.058770 · Cited by 16
Tsai, H.-C., Huang, Y.-F., & Kuo, C.-W. (2024) · Research Square · doi:10.21203/rs.3.rs-4022248/v1 · Cited by 21
A survey on applications of reinforcement learning in spatial resource allocation
Zhang, D., Wang, M., Mango, J., Li, X., & Xu, X. (2024) · Computational Urban Science · doi:10.1007/s43762-024-00127-z · Cited by 18
Devanathan, B., & Kamarasan, M. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-14164-5 · Cited by 15
Liu, X., Zhang, W., Zhang, Q., Chen, L., Zeng, T., Zhang, J., Min, J., Tian, S., Zhang, H., Huang, H., et al. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.1043919 · Cited by 18
Sekaran, K., Ramanathan, G., Thirunavukarasu, R., Iyyadurai, R., Karthik, G., & Doss, C. G. P. (2023) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2023.02.003 · Cited by 15
Oscillometry in Lung Function Assessment: A Comprehensive Review of Current Insights and Challenges
Sarkar, S., Jadhav, U. S., Ghewade, B., Sarkar, S., & Wagh, P. B. (2023) · Cureus · doi:10.7759/cureus.47935 · Cited by 15
The Benefit of Artificial Intelligence in the Analysis of Malignant Brain Diseases: A Mini Review
Al-Shahwani, H., & Faieq, A. K. (2023) · Mesopotamian Journal of Artificial Intelligence in Healthcare · doi:10.58496/mjaih/2023/011 · Cited by 15
Artificial Intelligence-Driven Enhancements in Medical Tourism
Bathla, G., Raina, A., & Rana, V. S. (2024) · Advances in hospitality, tourism and the services industry (AHTSI) book series · doi:10.4018/979-8-3693-2248-2.ch006 · Cited by 15
Babu, T., Nair, R. R., & Ebin, P. M. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-3278-8.ch007 · Cited by 15
Evaluating artificial intelligence for medical imaging: a primer for clinicians
Keni, S. (2024) · British Journal of Hospital Medicine · doi:10.12968/hmed.2024.0312 · Cited by 19
Yapa, P. N., Rajapaksha, S., & Munaweera, I. (2025) · Next research. · doi:10.1016/j.nexres.2025.100330 · Cited by 16
A predictive model for classifying colorectal cancer using principal component analysis
Arowolo, M. O., Aigbogun, H. E., Michael, P. E., Adebiyi, M. O., & Tyagi, A. K. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-98352-5.00004-5 · Cited by 15
Bridging the equity gap towards inclusive artificial intelligence in healthcare diagnostics
Chan, S. C. C., Neves, A. L., Majeed, A., & Faisal, A. A. (2024) · BMJ · doi:10.1136/bmj.q490 · Cited by 15
AI Workflow, External Validation, and Development in Eye Disease Diagnosis
Chen, Q., Keenan, T. D. L., Agrón, E., Allot, A., Guan, E., Duong, B., Elsawy, A. S., Hou, B., Xue, C., Bhandari, S., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.17204 · Cited by 14
Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges
Wang, M., Chang, W., & Zhang, Y. (2025) · MedComm · doi:10.1002/mco2.70460 · Cited by 22
Choi, H., Lee, J. Y., Sul, Y. H., Kim, S., Ye, J. B., Lee, J. S., Yoon, S., Seok, J., Han, J., Choi, J. H., et al. (2023) · Medicine · doi:10.1097/md.0000000000034847 · Cited by 14
Analysis of the Priorities and Perspectives in Artificial Intelligence Implementation
Kondratenko, Y. P., Shevchenko, A., Zhukov, Y., Slyusar, V. I., Kondratenko, G., Klymenko, M., & Striuk, O. (2023) · International Conference on Dependable Systems, Services and Technologies (DESSERT) · doi:10.1109/dessert61349.2023.10416432 · Cited by 14
Yadav, S., Rawal, G., Jeyaraman, M., & Jeyaraman, N. (2024) · Cureus · doi:10.7759/cureus.57311 · Cited by 14
Data-Driven Enterprise Architecture for Pharmaceutical R&D
Uzhakova, N., & Fischer, S. (2024) · Digital · doi:10.3390/digital4020017 · Cited by 14
Bangash, S. H., Khan, I., Husnain, G., Irfan, M. A., & Iqbal, A. (2024) · VFAST Transactions on Software Engineering · doi:10.21015/vtse.v12i1.1760 · Cited by 17
Artificial Intelligence in Eye Movements Analysis for Alzheimer’s Disease Early Diagnosis
Maleki, S. F., Yousefi, M., Sobhi, N., Jafarizadeh, A., Alizadehsani, R., & Górriz, J. M. (2024) · Current Alzheimer Research · doi:10.2174/0115672050322607240529075641 · Cited by 15
Nabrdalik, K., Irlik, K., Meng, Y., Kwiendacz, H., Piaśnik, J., Hendel, M., Ignacy, P., Kulpa, J., Kegler, K., Herba, M., et al. (2024) · Cardiovascular Diabetology · doi:10.1186/s12933-024-02367-z · Cited by 16
Artificial intelligence in cardiovascular procedures: a bibliometric and visual analysis study
Gadhachanda, K. R., Marsool, M. D. M., Bozorgi, A., Ameen, D., Nayak, S. S., Nasrollahizadeh, A., Alotaibi, A. S., Farzaei, A., Keivanlou, M., Hassanipour, S., et al. (2025) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000003112 · Cited by 16
Pancreatic Tumor‐Targeting Stemsome Therapeutics
Park, J., Park, J. Y., Jeong, Y., Park, J., Park, Y. H., Kim, S., & Khang, D. (2023) · Advanced Materials · doi:10.1002/adma.202300934 · Cited by 14
Thetbanthad, P., Sathanarugsawait, B., & Praneetpolgrang, P. (2025) · Journal of Imaging · doi:10.3390/jimaging11010011 · Cited by 14
Mauro, L. D., Capasso, E., Tettamanti, C., Casella, C., Francaviglia, M., Volonnino, G., Rinaldi, R., Esposito, M., & Chisari, M. G. (2025) · Journal of Forensic and Legal Medicine · doi:10.1016/j.jflm.2025.102941 · Cited by 16
AlMohimeed, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-16415-5 · Cited by 13
Galani, A., Zikopoulos, A., Moustakli, E., Potiris, A., Paraskevaidi, M., Arkoulis, I., Machairoudias, P., Stavrakaki, S. M., Kyrgiou, M., & Stavros, S. (2025) · Cancers · doi:10.3390/cancers17193179 · Cited by 17
The Role of Artificial Intelligence in Shaping the Doctor–Patient Relationship: A Narrative Review
Merlo, E. M., Sparacino, G., Silvestro, O., Giacobello, M. L., Meduri, A., Casciaro, M., Gangemi, S., & Martino, G. (2026) · Healthcare · doi:10.3390/healthcare14040481 · Cited by 16
Ma, L., Huang, L., Chen, Y., Zhang, L., Nie, D., He, W., & Qi, X. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1133491 · Cited by 15
Musleh, A. M., AlRyalat, S. A., Abid, M. N., Salem, Y., Hamila, H. M., & Sallam, A. B. (2023) · Survey of Ophthalmology · doi:10.1016/j.survophthal.2023.11.010 · Cited by 13
Gu, Z., Krishna, K. V. M., Parsazadeh, M., Sharma, S., Manjunath, A., Tran, H., Fu, S., & Dahotre, N. B. (2024) · Progress in Additive Manufacturing · doi:10.1007/s40964-024-00603-2 · Cited by 15
Reshi, A. A., Shafi, S., Qayoom, I., Wani, M., Parveen, S., & Ahmad, A. (2024) · International Journal of experimental research and review · doi:10.52756/ijerr.2024.v38.017 · Cited by 18
Horwath, J. P., Lin, X., He, H., Zhang, Q., Đufresne, E. M., Chu, M., Sankaranarayanan, S. K. R. S., Chen, W., Narayanan, S., & Cherukara, M. J. (2024) · Nature Communications · doi:10.1038/s41467-024-49381-z · Cited by 13
Uraif, A. (2024) · Communications and Network · doi:10.4236/cn.2024.163003 · Cited by 15
Applications of Artificial Intelligence in Thrombocytopenia
Elshoeibi, A. M., Ferih, K., Elsabagh, A. A., Elsayed, B., Elhadary, M. R., Marashi, M., Wali, Y. A. M. S., Al‐Rasheed, M., Al-Khabori, M. K., Osman, H. Y., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13061060 · Cited by 13
Deep Reinforcement Learning-Based Retinal Imaging in Alzheimer’s Disease: Potential and Perspectives
Hui, H. Y. H., Ran, A. R., Dai, J. J., & Cheung, C. Y.-L. (2023) · Journal of Alzheimer s Disease · doi:10.3233/jad-230055 · Cited by 13
Korenevskiy, N. A., Belozerov, V. A., Al-Kasasbeh, R. T., Al-Smadi, M. M., Krutskikh, V. V., Shalimova, E. V., Al-Jundi, M., Rodionova, S. N., Filist, S., Shaqadan, A. A., et al. (2023) · Critical Reviews in Biomedical Engineering · doi:10.1615/critrevbiomedeng.2023049762 · Cited by 13
Detjen, H., Densky, L., von Kalckreuth, N., & Kopka, M. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713898 · Cited by 16
Mapping study on AI-based technologies in palliative care – a scoping study
Silva-Ferreira, M., Cruz, S., Luís, M., Silva, M. C., Monteiro‐Reis, S., Henrique, R., Jerónimo, C., Lefèvre, S.-C., LAPLAUD, A., Frasca, M., et al. (2025) · BMC Palliative Care · doi:10.1186/s12904-025-01909-w · Cited by 13
Data Challenges for Externally Controlled Trials: Viewpoint
Velummailum, R., McKibbon, C., Brenner, D. R., Stringer, E. A., Ekstrom, L. B., & Dron, L. (2023) · Journal of Medical Internet Research · doi:10.2196/43484 · Cited by 12
Khan, M., Hanna, C., Findlay, M. C., Lucke-Wold, B. P., Karsy, M. J., & Jensen, R. L. (2023) · Neurosurgery Clinics of North America · doi:10.1016/j.nec.2023.02.014 · Cited by 12
Saßmannshausen, S. M., Ontika, N. N., de Carvalho, A. F. P., Rouncefield, M., & Pipek, V. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642362 · Cited by 14
Artificial intelligence in health care: A review of uses, challenges and potential uses
Makade, J. G., Bankar, N. J., Kumar, A., Bandre, G. R., & Yenurkar, G. K. (2024) · AIP conference proceedings · doi:10.1063/5.0240230 · Cited by 12
AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation
Narigina, M., Vindecs, A., Bošković, D., Merkuryev, Y., & Romānovs, A. (2025) · Future Internet · doi:10.3390/fi17050204 · Cited by 12
AI in Medical Questionnaires: Scoping Review
Luo, X., Li, Y. L., Xu, J., Zheng, Z., Ying, F., & Huang, G. (2025) · Journal of Medical Internet Research · doi:10.2196/72398 · Cited by 15
A Proactive Explainable Artificial Neural Network Model for the Early Diagnosis of Thyroid Cancer
Aljameel, S. S. (2022) · Computation · doi:10.3390/computation10100183 · Cited by 12
Paulraj, K., Soms, N., Azariya, S. D. S., Priya, S. S., Emilyn, J. J., & Sureshkumar, V. (2023) · OITS International Conference on Information Technology (OCIT) · doi:10.1109/ocit59427.2023.10431108 · Cited by 12
Optimizing Hyperparameters for Improved Melanoma Classification using Metaheuristic Algorithm
Adamu, S., Alhussian, H., Aziz, N., Abdulkadir, S. J., Alwadin, A., Imam, A. A., Garba, A., & Saidu, Y. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0141057 · Cited by 11
iPREDICT: AI enabled proactive pandemic prediction using biosensing wearable devices
Riaz, M. S., Shaukat, M., Saeed, T., Ijaz, A., Qureshi, H. N., Posokhova, I., Sadiq, I., Rizwan, A., & Imran, A. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101478 · Cited by 14
Singh, D., Das, P., & Ghosh, I. (2024) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-024-00433-0 · Cited by 11
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
