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The treatment of cancer is undergoing a significant transformation, moving beyond traditional approaches like chemotherapy, radiation, and surgery. According to health experts, artificial intelligence (AI) is making substantial strides in cancer treatment, benefiting both doctors and patients by improving outcomes.
Health professionals emphasize that AI’s role in oncology extends from drug development to treatment prediction and prognosis. AI also fosters the growth of personalized medicine, tailoring treatments to individual patient needs. However, concerns about data privacy, safety, and the ethical use of patient data remain prevalent.
“As a surgical oncologist, I can confidently state that AI is no longer limited to surgery, chemotherapy, targeted therapy, or radiation,” said Raj Nagarkar, Managing Director & Chief of Surgical Oncology & Robotic Services at HCG Manavata Cancer Centre (HCGMCC) & Hospitals. “AI has profound implications in radio diagnostics and biomedical cancer research as well. It aids in creating new medications and treatments and facilitates early cancer detection through image analysis, a critical application we use for the early detection of oral cancers.”
Roheet Rao, AVP, IT & Oncology at Apollo Hospitals, added, “AI computer vision models are being explored in radio-imaging modalities for early detection of disease and cancer risk prediction. Point-of-care diagnostics startups are using AI algorithms for early detection with promising results. Early detection of disease has a significant impact on outcomes, and by employing AI, we can certainly improve cancer care delivery.”
AI-enabled screening for breast cancer is also assisting in designing and personalizing treatments. Machine-learning techniques in CT scans and MRIs improve imaging accuracy, enhancing the diagnosis of several cancers. In surgery, AI analyses of computer-assisted or robotic-assisted procedures are making operations safer, more precise, and more comfortable for patients.
Furthermore, AI in chemotherapy refines and customizes treatment options by analyzing datasets to tailor plans based on genetic and molecular characteristics. Predictive models can estimate patient responses to specific regimens, enhancing the effectiveness of treatments.
AI is also advancing options like immunotherapy and CAR T-cell therapy. “Deep learning models for cancer stem cell detection aid in early diagnosis and customising treatment plans, making AI a reality across all facets of oncology, from diagnosis and research to treatment,” noted Dr. Raj.
Despite the promising developments, there are risks associated with using AI in healthcare that need to be addressed. “One of the key issues is data privacy, safety, and ethical use of patient data,” Rao said. He also pointed out the potential biases in AI models, which stem from the training data used to generate them. “Unless there is validation of AI models across different types and cohorts of data, oncologists need to be mindful of such biases,” he added.
“The accuracy and reliability of the models need to be tested thoroughly both prior to and within clinical settings to ensure patient safety. Clinicians will always have the final say on clinical care. The only way to ensure oversight when using AI is to have a human-in-the-loop,” Rao concluded.
As AI continues to revolutionize cancer treatment, addressing these concerns will be crucial for its wider adoption and ensuring that it benefits all patients equitably.
