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RETRACTED ARTICLE: Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda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.
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“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”
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“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).”
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“Multi-omics approaches have identified targets like OsBADH2 and OsMPK2 in rice that could enhance drought resilience (Kumar, 2023).”
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“Targeting ABA signaling genes, such as PYL receptors, has improved drought tolerance across various species (Kumar, 2023).”
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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
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Olszewski, R., Watros, K., Mańczak, M., Owoc, J., Jeziorski, K., & Brzeziński, J. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105562 · Cited by 48
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Sarantopoulos, A., Mastori Kourmpani, C., Yokarasa, A. L., Makamanzi, C., Antoniou, P., Spernovasilis, N., & Tsioutis, C. (2024) · Tropical Medicine and Infectious Disease · doi:10.3390/tropicalmed9100228 · Cited by 40
Garg, P. K., Mohanty, A. R., Ramisetty, S. K., Kulkarni, P., Horne, D. A., Pisick, E. P., Salgia, R., & Singhal, S. S. (2023) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2023.189026 · Cited by 42
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Lebal, A., Moussaouı, A., & Rezgui, A. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13947-0 · Cited by 41
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Yang, Q., Bee, Y. M., Lim, C. C., Sabanayagam, C., Yim-Lui Cheung, C., Wong, T. Y., Ting, D. S. W., Lim, L.-L., Li, H., He, M., et al. (2025) · eClinicalMedicine · doi:10.1016/j.eclinm.2025.103089 · Cited by 38
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Wang, C., Li, M., He, J., Wang, Z., Darzidehkalani, E., Chen, Z., Ye, J., Li, T., Su, Y., Ke, J., et al. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103268 · Cited by 65
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Self-citationTajerian, A., Kazemian, M., Tajerian, M., & Malayeri, A. A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0284437 · Cited by 43
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Demir, R., Koc, S., Öztürk, D. G., Bilir, Ş., Özata, İ. H., Williams, R., Christy, J., Akkoç, Y., Tınay, İ., Gündüz-Demir, Ç., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-52728-7 · Cited by 37
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Bardideh, E., Alizadeh, F. L., Amiri, M., & Ghorbani, M. (2024) · American Journal of Orthodontics and Dentofacial Orthopedics · doi:10.1016/j.ajodo.2024.03.012 · Cited by 36
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Liu, F., Niu, Y., Zhang, Q., Wang, K., Dong, Z., Wong, I. N., Cheng, L., Li, T., Duan, L., Li, K., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102374 · Cited by 41
“46–48 Their ability to autonomously make data-driven decisions and provide real-time insights enhances both the efficiency and accuracy of medical decision-making, supporting healthcare professionals in delivering timely, personalized care.”
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Narasimhan, G., & Victor, A. (2025) · Scientific Reports · doi:10.1038/s41598-024-73867-x · Cited by 35
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Abonyi, H. N., Peter, I. E., Onwuka, A. M., Achile, P. A., Obi, C. B., Akunne, M. O., Ejikeme, P. M., Amos, S., Akunne, T. C., Attama, A. A., et al. (2024) · Nanomedicine · doi:10.1080/17435889.2024.2443385 · Cited by 32
Vu, T., Kokubo, Y., Inoue, M., Yamamoto, M., Mohsen, A. S., Morales, A. M., Inoué, T., Tay, J., Yoshizaki, M., Watanabe, N., et al. (2025) · JMIR Cardio · doi:10.2196/68066 · Cited by 41
Modi, K., Singh, I., & Kumar, Y. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09957-2 · Cited by 34
Self-citationBhardwaj, P., Kumar, S., & Kumar, Y. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09951-8 · Cited by 31
Self-citationHurvitz, N., & Ilan, Y. (2023) · Clinics and Practice · doi:10.3390/clinpract13040089 · Cited by 35
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Hassen, S. B., Néji, M., Hussain, Z., Hussain, A., Alimi, A. M., & Frikha, M. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127325 · Cited by 32
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Wibowo, M. F., Pyle, A., Lim, E. F. H., Ohde, J. W., Liu, N., & Karlström, J. (2025) · Journal of Medical Internet Research · doi:10.2196/71591 · Cited by 45
Markus, A., Carolus, A., & Wienrich, C. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100485 · Cited by 45
Wang, S., Zhou, X., Li, C., Wang, S., Li, Y., Tan, T., & Zheng, H. (2025) · Research · doi:10.34133/research.1029 · Cited by 29
Koul, A., Bawa, R. K., & Kumar, Y. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18381-y · Cited by 32
Self-citationMondal, S., Maity, R., & Nag, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-85765-x · Cited by 29
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Alsanosi, S., & Padmanabhan, S. (2024) · Healthcare · doi:10.3390/healthcare12070788 · Cited by 38
Abbas, S., Qaisar, A., Farooq, M. S., Saleem, M. A., Ahmad, M., & Khan, M. A. (2024) · Sensors · doi:10.3390/s24206618 · Cited by 28
Chen, Z., Hao, J., Sun, H., Li, M., Zhang, Y., & Qian, Q. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02870-7 · Cited by 29
Rezaee, K., Khosravi, M. R., Jabari, M., Hesari, S., Anari, M. S., & Aghaei, F. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.23041 · Cited by 28
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Padhi, D. K., Padhy, N., Bhoi, A. K., Shafi, J., & Yesuf, S. H. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7588303 · Cited by 32
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Bogoslov, I. A., Corman, S., & Lungu, A. E. (2024) · Sustainability · doi:10.3390/su16114579 · Cited by 26
Gurnani, B., Kaur, K., Lalgudi, V. G., Kundu, G., Mimouni, M., Liu, H., Jhanji, V., Prakash, G., Roy, A., Shetty, R., et al. (2024) · Journal Français d Ophtalmologie · doi:10.1016/j.jfo.2024.104242 · Cited by 27
Chauhan, A. S., Singh, R., Priyadarshi, N., Twala, B., Suthar, S., & Swami, S. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00161-0 · Cited by 28
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Kotola, B. S., & Teklu, S. W. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/9977727 · Cited by 26
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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
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