Challenges of Artificial Intelligence in Cancer Diagnosis: An Update on Future and Prospects

Articles in Press

Document Type : Review Article/ Systematic Review Article/ Meta Analysis

Authors

1 Faculty of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran

2 Pharmacy graduate, Faculty of Pharmacy, Ayatollah Amoli Branch, Islamic Azad University, Iran

3 Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran

4 School of Medicine, Novosibirsk State University

5 Electrical Department, Faculty of Engineering, Islamic Azad University-South Tehran Branch, Tehran, Iran

6 Medical Doctor, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

Abstract

Background: Artificial intelligence (AI) has become increasingly prominent in the medical field, particularly in the diagnosis of cancer.
 
Objectives: This comprehensive review was conducted to review the challenges of AI in cancer diagnosis.
 
Methods: This comprehensive review was conducted through a systematic search of major scientific databases, including PubMed, Scopus, Web of Science, and IEEE Xplore, utilizing a combination of keywords and Medical Subject Headings (MeSH) terms such as “artificial intelligence,” “machine learning,” “deep learning,” “neural networks,” “cancer diagnosis,” “oncological imaging,” “pathology,” “biomarkers,” and “precision oncology,” covering the period from January 2019 to December 2024 to capture the most relevant and impactful studies in this rapidly evolving field. The inclusion criteria were focused on peer-reviewed original research articles, significant review papers, and high-impact conference proceedings that demonstrated a direct application of AI algorithms in diagnostic procedures, while exclusion criteria encompassed non-English publications, studies with insufficient methodological detail, articles not focused on diagnostic applications, and editorials or opinion pieces without original data, ensuring a robust and evidence-based analysis of the current landscape.
 
Results: The challenges in the widespread utilization of this technology in clinical settings are discussed. Deep learning algorithms, especially convolutional neural networks (CNN), can identify suspicious areas in mammograms, CT scans, and MRI images that doctors may easily overlook. These capabilities improve accuracy and reduce human errors in cancer diagnosis. In addition to image analysis, AI can also analyze patients' molecular and genetic data. Using genomic and proteomic data, this technology can identify gene mutations and specific biological markers of cancer. As a result, early diagnosis and selection of targeted patient treatments are carried out with greater accuracy. However, despite significant progress in this field, several challenges remain, including the accurate interpretation of data, the need for substantial training data, and the ability to generalize algorithms to diverse populations.
 
Conclusion: In conclusion, AI is fundamentally augmenting the field of cancer diagnostics, moving from a theoretical promise to a powerful clinical tool. The evidence demonstrates that AI algorithms, particularly deep learning models, offer significant and measurable benefits.

Keywords

Acknowledgements: Not applicable.

 

Availability of data and materials: The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

 

Conflicts of interests: The authors declare no conflict of interest.

 

Consent for publication: Not applicable.

 

Ethics approval and consent to participate: The present study was conducted in terms of the principles of the revised Declaration of Helsinki, which is a statement of ethical principles that directs physicians and other participants in medical research involving human subjects.

 

Financial disclosure: No funding was received to conduct this study.

 

Author contributions: Conceptualization: [Alireza Omeanzadeh], Methodology: [All authors]; Formal analysis and investigation: [All authors]; Writing-original draft preparation: [All authors]; Writing - review and editing: [All Authors]; Funding acquisition: [Self-funding]; Supervision: [Alireza Omeanzadeh]. All authors checked and approved the final version of the manuscript for publication in the present journal.

 

Open Access Policy: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/

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Razavi Journal of Medicine

Articles in Press, Accepted Manuscript
Available Online from 02 September 2025

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