AI in Cancer Therapy - Smarter, Faster, Better Treatment: A Systemic Review

Abstract

AI has revolutionized cancer therapy, transforming it into a smarter, faster, and more effective
paradigm. Machine learning algorithms excel in image analysis, detecting tumors in mammograms,
CT scans, and MRIs with precision surpassing human radiologists often achieving 95% accuracy in
early-stage lung cancer detection. Deep learning models predict patient responses to chemotherapy
and immunotherapy by analyzing genomic data, histopathological images, and electronic health
records, enabling precision medicine that tailors therapies like CAR-T cells or targeted kinase
inhibitors to individual profiles. Key advancements include AI-driven drug discovery, where
generative adversarial networks (GANs) simulate molecular interactions to identify novel
compounds, slashing development timelines from years to months. Robotic surgery assisted by AI,
such as da Vinci systems with real-time anomaly detection, minimizes invasiveness and recurrence
rates. Predictive analytics forecast disease progression and side effects, supporting adaptive dosing
in radiotherapy via tools like convolutional neural networks (CNNs). Challenges persist, including
data biases, interpretability of "black-box" models, regulatory hurdles, and ethical concerns over
equity in access. Yet, hybrid AI-human workflows promise to mitigate these. Clinical trials, like those
using IBM Watson for leukemia prognostics, demonstrate up to 30% survival improvements. Looking
ahead, federated learning and quantum-enhanced AI could further accelerate breakthroughs. This
article synthesizes recent evidence, highlighting AI's potential to make cancer therapy not just
smarter and faster, but decisively better paving the way for curative strategies in an era of
exponential data growth.