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RETRACTED ARTICLE: Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda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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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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