Perspective - Journal of Clinical Oncology and Cancer Research (2025) Volume 8, Issue 1

Role of Artificial Intelligence in Precision Oncology and Cancer Diagnosis

Article type:

Home Page URL:  https://www.alliedacademies.org/journal-clinical-oncology-cancer-research/

Journal short name: Allied J Clin Oncol Cancer Res

Volume: 8

Issue: 1

PDF No: 179

Citation: Malhotra A. Role of Artificial Intelligence in Precision Oncology and Cancer Diagnosis. J Oral Med Surg. 2025; 8(1):179

^*Correspondence to: Arjun Malhotra. Department of Oncology Research, Faculty of Medicine, Meditech University, India. Email: arjun.malhotra@meditech.edu

Received: 27-May-2025, Manuscript No. AACOCR-25-169757; Editor assigned: 01-Jun-2025, PreQC No. AACOCR-25-169757 (PQ); Reviewed: 15- Jun-2025, QC No. AACOCR-25-169757; Revised: 22- Jun-2025, Manuscript No. AACOCR-25-169757 (R); Published: 29- Jun-2025, DOI:10.35841/AACOCR-8.1.179

Role of Artificial Intelligence in Precision Oncology and Cancer Diagnosis

Arjun Malhotra*                        

Department of Oncology Research, Faculty of Medicine, Meditech University, India

Introduction

The advent of Artificial Intelligence (AI) has revolutionized the field of oncology, paving the way for precision medicine approaches that are more accurate, personalized, and efficient than traditional cancer care models. AI technologies—particularly machine learning (ML) and deep learning algorithms—are enabling oncologists to analyze vast datasets from genomic profiles, histopathological images, and clinical records with unprecedented speed and accuracy. These tools not only enhance early cancer detection but also refine prognostic assessments and guide individualized treatment strategies based on a patient’s unique tumor biology [1, 2, 3, 4, 5].

In cancer diagnosis, AI-powered image analysis systems have demonstrated remarkable capabilities in identifying subtle patterns in radiology and pathology images that may elude the human eye, reducing diagnostic errors and enabling earlier intervention. Furthermore, AI-driven predictive models integrate molecular data with clinical variables to optimize therapeutic decisions, improving patient outcomes while minimizing unnecessary treatments. As research in precision oncology advances, AI continues to bridge the gap between complex biological data and actionable clinical insights.

Conclusion

Artificial Intelligence stands as a transformative force in precision oncology, offering unparalleled potential to improve cancer detection, characterization, and treatment personalization. By harnessing the power of large-scale data analytics, AI enables clinicians to predict disease trajectories, tailor therapies, and monitor treatment responses with remarkable precision. The integration of AI into clinical oncology not only enhances diagnostic accuracy but also accelerates the discovery of novel biomarkers and therapeutic targets, ultimately moving cancer care towards a more patient-centered and outcome-driven paradigm. As AI technologies mature, their ethical application, transparency, and validation will be critical in ensuring they fulfill their promise of delivering better health outcomes for cancer patients worldwide.

References

References

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