PREDICTING CANCER RECURRENCE WITH AI-ENHANCED IMAGING: A REVIEW OF PREDICTIVE MODELS AND THEIR CLINICAL IMPLICATIONS

Artificial intelligence (AI) is transforming healthcare by improving the prediction of cancer recurrence, which is critical for guiding timely interventions. However, imbalanced medical datasets present challenges, making it essential to use more than just accuracy as a performance metric. Metrics like sensitivity, specificity, and precision are crucial for evaluating how well models detect recurrences without producing false predictions. A review of existing literature shows AI models increasingly outperform traditional diagnostic methods, especially through the integration of multi-modal data.Accuracy alone may misrepresent a model’s performance, especially with unbalanced datasets where one outcome (recurrence or non-recurrence) dominates. Sensitivity measures a model's ability to identify true recurrences, while specificity focuses on reducing false positives. Precision ensures high-risk predictions are reliable, minimizing unnecessary treatments. Incorporating multi-modal data, such as medical imaging, genomic profiles, and liquid biopsies, further improves diagnostic accuracy. Case studies reveal that AI models integrating these data types show superior sensitivity and specificity, particularly in early detection. Liquid biopsies, for example, enable non-invasive recurrence detection, allowing personalized treatments to be initiated earlier.AI holds immense potential to improve cancer care by enabling earlier and more precise predictions, but certain challenges persist. Issues like ethical concerns, data standardization, and clinical variability need to be addressed to ensure widespread adoption and effectiveness. Ongoing research is essential to refine these models, ensuring their reliability and usability in real-world clinical settings. Properly implemented AI-powered predictive models can significantly enhance patient outcomes by supporting earlier interventions and guiding personalized treatment strategies.