Artificial Intelligence in Cancer: A SWOT Analysis

Year 2024, Volume: 8 Issue: 1, 107 - 137, 31.12.2024

Gülşah Torkay , Nouran Fadlallah , Ahmet Karagöz , Mesut Canlı , Ezgi Saydam , Ayşenur Mete , Furkan Kızılışık , Hakan Darici , Yusuf Yeşil

https://doi.org/10.61969/jai.1469589

Cited By: 1

Abstract

Cancer, a collection of maladies that has undergone extensive examination over centuries, remains a formidable challenge. Despite the array of available pharmacological and therapeutic interventions, the intricate molecular dynamics and heterogeneity of cancer continue to challenge the scientific community. Artificial Intelligence (AI) emerges as a promising avenue, offering the potential for expedited, precise diagnostics devoid of human expertise. Additionally, AI facilitates the tailoring of patient-specific therapeutic strategies targeting various facets of cancer, spanning macroscopic to microscopic levels. Nonetheless, it is imperative to scrutinize the potential benefits and limitations of AI technologies in this context. This review undertakes a comprehensive Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of AI's application in cancer. An extensive compilation of AI applications encompasses predictive modeling, diagnostic capabilities, prognostic assessments, and personalized therapeutic modalities, spanning genomic analyses to individualized treatment regimens. The synthesis of evidence suggests that the advantages of AI outweigh its drawbacks; nevertheless, obstacles to its widespread integration persist.

Keywords

Artificial intelligence, cancer, deep learning, machine learning, precision oncology, SWOT analysis

Supporting Institution

Scientific and Technological Research Council of Türkiye (TUBITAK)

Project Number

Yok

Thanks

The authors would like to acknowledge the financial support from the Scientific and Technological Research Council of Türkiye (TUBITAK) 2210-A General Domestic Graduate Scholarship Program and 2211-E National PhD Scholarship Program for Former Undergraduate and MSc/MA Scholars (App No: 1649B022101483 and App No: 1649B032304943).

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