Artificial intelligence (AI) has emerged as a transformative influence in the realm of radiation oncology, holding the potential to reshape diagnostic and therapeutic approaches significantly. In a discussion with CancerNetwork®, Dr. Richard Bakst delved into the pivotal role of AI in radiology and its implications for advancing patient care and medical procedures.
Dr. Bakst, a radiation oncologist at Mount Sinai, underscored the significance of AI in medical radiology, particularly in the context of lung cancer screening and socioeconomic interpretation support. He also highlighted AI’s prospective contributions to medical radiology, citing its ability to detect minute anomalies in imaging scans and aid in delineating normal structures, thereby offering promise for refining treatment strategies.
Envisioning a future where AI becomes a valuable asset in therapy, Dr. Bakst foresees its impact on contouring volumes, identifying risks associated with microscopic diseases, and facilitating tumor volume delineation. This integration of artificial intelligence is poised to significantly enhance the precision and efficacy of cancer treatment protocols.
The increasing integration of AI is set to revolutionize the field of radiology by enabling the identification of subtle irregularities, enhancing treatment planning processes, and refining targeting precision.
As AI permeates various domains, its profound influence on medical radiology, lung cancer screening, demographic analyses, and other medical specialties becomes increasingly evident. Its eventual foray into treatment modalities, which is already underway in certain contexts, promises to streamline processes significantly. Leveraging AI for identifying minuscule pathologies in imaging studies simplifies the contouring of standard structures, paving the way for advancements in radiotherapy planning. The imminent future holds the potential for AI to impact volume design, assess the risk of microscopic diseases, and aid in tumor removal procedures. This impending transformation will empower practitioners to discern optimal treatment volumes, tailor patient normalization based on risk profiles, and refine cancer targeting strategies.
