Artificial Intelligence (AI) has become deeply integrated into the healthcare industry — from disease diagnosis and treatment, to patient care and preventive medicine.
In the field of oncology, AI applications have emerged across multiple dimensions, including cancer genomics analysis, medical imaging and digital pathology, electronic medical record (EMR) integration and research, and cancer drug development.

By Bryant Chen, Medical Affairs Manager, Be Accelerator

Cancer Genomic Data Analysis

When internal or external factors trigger genetic mutations that accumulate and affect cell growth and survival, cancer may develop. Approximately 10–15% of cancer patients inherit specific gene mutations that predispose them to the disease.
Therefore, genomic profiling of patients has become a crucial step in precision oncology.

For instance, in melanoma, genomic sequencing can identify mutations in MEK (Mitogen-Activated Protein Kinase) or BRAF (B-Raf Serine/Threonine Kinase), as well as the expression of immune checkpoint markers PD-1/PD-L1 (Programmed Cell Death Protein 1 / Ligand 1) — all of which are key references for selecting appropriate targeted or immunotherapy drugs.

However, analyzing such high-volume genomic data is highly complex, requiring specialized expertise and time. AI can significantly enhance the accuracy and speed of these analyses.
AI-based systems are now capable of identifying gene functions, regulatory regions, intergenic interactions, and data errors, while also cross-referencing with large public genomic databases such as TCGA and GenBank.
By integrating genomic with clinical data, AI helps oncologists formulate personalized treatment strategies, moving toward truly individualized cancer care.

Medical Imaging and Digital Pathology Applications

In cancer diagnosis and monitoring, imaging modalities such as X-ray, ultrasound, CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), and SPECT (Single Photon Emission CT) are routinely employed, along with digital pathology.

With the advancement of deep learning, many AI-assisted imaging and pathology tools have been approved by regulatory authorities:

• VBrain (Vysioneer, Inc.) – an AI-powered brain tumor auto-segmentation system that reduces “visual blind spots” during tumor identification.

• GI Genius (Medtronic, Plc.) – compatible with FDA-approved endoscopic systems, capable of detecting suspicious colorectal polyps during colonoscopy.

• Paige Prostate (Paige AI, Inc.) – analyzes prostate biopsy slides to locate regions most likely to contain malignant cells.

• Oleena (Voluntis, Inc.) – an FDA-cleared software for real-time symptom tracking and self-management of cancer treatment side effects, providing feedback directly to clinicians.

The integration of AI tools not only enhances diagnostic accuracy and reduces clinician workload, but also shortens treatment cycles—delivering substantial benefits for precision oncology.

EMR Integration and Research Optimization

Patient information—including clinical data, imaging, pathology, genomics, and quality-of-life metrics—collectively represents a massive pool of medical data.
However, the volume and complexity of these datasets make manual integration impractical.

AI systems can aggregate and structure data from EMRs, diagnostic imaging, pathology reports, and genetic sequencing.
They enable conditional searches, cohort analysis, and longitudinal tracking—linking diagnosis, prescriptions, treatment plans, and clinical outcomes.
This allows for real-time insights into treatment effectiveness and significantly optimizes the quality and speed of cancer care delivery.

AI-Driven Cancer Drug Discovery

Traditional cancer drug development is resource-intensive and time-consuming, often failing due to incorrect target selection, poor molecular binding, or inadequate trial populations.
Today, pharmaceutical companies increasingly leverage AI to accelerate and de-risk drug discovery.

AI can assist in:

• Molecular structure mining and automated compound design

• Predicting target compatibility, off-target effects, and toxicity

• Large-scale analysis of cellular and animal model data

• Mining vast biomedical literature for insights

These capabilities reduce laboratory bottlenecks, lower R&D costs, and significantly shorten drug discovery timelines, paving the way for smarter, faster cancer therapeutics.

Looking Ahead

The integration of AI across these four pillars—genomic analysis, imaging, data integration, and drug discovery—signals a new era in precision oncology.
Through data-driven intelligence, cancer care is evolving to become more accurate, personalized, and efficient, improving survival outcomes and redefining the future of medicine.

For more information, please contact:Bryant.chen@be.tworg.app