Advanced AI-driven image fusion techniques in lung cancer diagnostics: systematic review and meta-analysis for precisionmedicine

Abstract

Purpose

This paper aims to critically evaluate the role of advanced artificial intelligence (AI)-enhanced image fusion techniques in lung cancer diagnostics within the context of AI-driven precision medicine.

Design/methodology/approach

We conducted a systematic review of various studies to assess the impact of AI-based methodologies on the accuracy and efficiency of lung cancer diagnosis. The focus was on the integration of AI in image fusion techniques and their application in personalized treatment strategies.

Findings

The review reveals significant improvements in diagnostic precision, a crucial aspect of the evolution of AI in healthcare. These AI-driven techniques substantially enhance the accuracy of lung cancer diagnosis, thereby influencing personalized treatment approaches. The study also explores the broader implications of these methodologies on healthcare resource allocation, policy formation, and epidemiological trends.

Originality/value

This study is notable for both emphasizing the clinical importance of AI-integrated image fusion in lung cancer treatment and illuminating the profound influence these technologies have in the future AI-driven healthcare systems.

Citation

Sun, M. and Cui, C. (2024), "Advanced AI-driven image fusion techniques in lung cancer diagnostics: systematic review and meta-analysis for precisionmedicine", Robotic Intelligence and Automation, Vol. 44 No. 4, pp. 579-593. https://doi.org/10.1108/RIA-01-2024-0008