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Specialized LLMs, AI-assisted CT scans for early detection, and foundational models democratizing pathology are among emerging technologies in oncology care.
This section highlights AI's role in transforming oncology clinical workflows, particularly through specialized Large Language Models (LLMs) and agentic AI. These tools streamline documentation, aid in clinical decision-making, support tumor boards, and improve clinical trial matching and care coordination, aiming to reduce clinician burden and accelerate processes.
AI is enhancing early cancer detection in radiology, specifically in mammography and CT scans for pancreatic cancer. It helps reduce diagnostic review time, standardizes lesion measurement (RECIST assessments), and shows promise for broader real-world implementation beyond current specific use cases.
Foundational AI models are set to improve clinical processes in cardio-oncology, especially for analyzing electrocardiograms (ECGs) to detect arrhythmias or cardiovascular risk factors in cancer patients. These models are expected to receive expedited FDA approval and integrate into electronic medical records to 'move the needle' in cardio-oncology care.
AI, including transformer-based architectures, is advancing breast cancer screening by predicting recurrence risk and optimizing response scores. AI in radiomics can help predict treatment benefits from CT scans, and it has shown superior performance in reading breast imaging studies, including 3D mammograms, offering a more efficient screening tool.
Addressing the global pathologist shortage, foundational AI models are proposed to democratize pathologic oncology. These models can quickly analyze whole slide images, provide immediate initial screening readouts, and identify potential biomarkers remotely, accelerating diagnosis and treatment in underserved regions.
The year 2026 is projected as pivotal for AI industrialization in oncology, with significant investments from health and pharmaceutical companies to accelerate drug discovery and patient deployment. Emphasis is placed on oversight, clinical involvement, and governance to ensure AI tools are effective, safe, and free from misinformation. Patient-facing AI platforms like ChatGPT Health and Claude are also emerging to assist with health-related decisions.