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

• Published in: Assembly automation Vol. 44; no. 4; pp. 579 - 593
• Main Authors: Sun, Meiling, Cui, Changlei
• Format: Journal Article
• Language: English
• Published: Bingley Emerald Group Publishing Limited 18.07.2024
• Subjects: Algorithms; Artificial intelligence; Clinical outcomes; Computer vision; Customization; Diagnosis; Health care; Health services; Image enhancement; Keywords; Libraries; Lung cancer; Magnetic resonance imaging; Medical diagnosis; Medical imaging; Medical prognosis; Metastasis; Mortality; Resource allocation; Survival analysis; Systematic review; Tomography; Trends
• ISSN: 2754-6969; 2754-6977
• DOI: 10.1108/RIA-01-2024-0008

PurposeThis 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/approachWe 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.FindingsThe 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/valueThis 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.