Harnessing AI for Precision Oncology: Transformative Advances in Non-Small Cell Lung Cancer Treatment

Abstract

This systematic review examines the emerging role of Artificial Intelligence (AI) in planning and optimizing treatment for Non-Small Cell Lung Cancer (NSCLC). Focusing on patient-tailored therapy planning and enhancing treatment efficacy through advanced deep learning algorithms, we meticulously selected and analyzed thirteen high-quality research studies demonstrating AI’s integration in NSCLC management. These studies show the ability of AI to process complex clinical, radiomic, and genomic data to provide personalized therapy plans. AI technologies, such as deep learning models and machine learning, have shown exceptional promise in predicting immune responses to initial treatments, potentially revolutionizing the management of NSCLC. This review highlights AI’s transformative impact on predicting treatment outcomes, optimizing therapy regimens, and improving decision-making processes in NSCLC treatment. The collective findings from these studies reveal a significant trend towards personalized medical approaches, showcasing AI’s remarkable capacity to handle extensive datasets and forecast individual patient reactions. This reassures us about the efficiency of AI in managing complex information, thereby increasing treatment efficacy and improving patient health outcomes. However, this review also underscores the pressing need for further research and development in AI applications, highlighting the urgency and importance of this field. Integrating AI into NSCLC treatment marks a new era of precision cancer care, paving the way for more accurate, efficient, and patient-centered care. The challenges and limitations identified in this review serve as a call to action, urging the oncology community to continue pushing the boundaries of AI in cancer care. This review aims to identify the most advanced and effective technologies, enabling oncology researchers and healthcare professionals to utilize these tools without having to search through various available sources. This approach aims to streamline access to crucial information, allowing practitioners to focus on recent advancements. For this reason, the study concentrates on the last two years, which have been marked by significant integration of AI into precision medicine.

Ähnliche Arbeiten