AI-Powered Precision Oncology Applications and Data Analysis

Artificial intelligence applications play a critical role in the transition from traditional cancer treatment approaches to precision oncology based on molecular profiles.

Clinical Practice and Increase in Genomic Data

Artificial intelligence technologies are becoming increasingly visible in clinical documentation, evidence retrieval, and digital triage processes. Traditional methods in cancer treatment based on tumor location and stage are evolving toward precision oncology guided by molecular profiles.

Biomarkers and Data Integration

Standard-of-care predictive biomarkers recorded a significant increase between 2017 and 2022. Large volumes of data generated by precision oncology are transformed into actionable information for treatment design thanks to artificial intelligence.

Genetic Sequencing and Matching

Next-generation sequencing methods detect mutations, fusions, and tumor markers, associating them with appropriate treatments through various databases. Clinical trials such as ROME show that genomically matched therapies significantly increase response rates.

Molecular Tumor Boards and Language Models

Molecular tumor boards utilize large language models to conduct literature reviews and match patients with clinical trials. However, the risk of hallucination in artificial intelligence systems necessitates expert physician supervision.

Drug Response and Heterogeneity Solutions

While deep learning and multimodal models are used to predict drug responses, single-cell-based personalization tools address tumor heterogeneity. These approaches provide significant advantages in treatment planning.

Combination Therapies and Challenges

Combination therapy optimization tools help predict synergistic drug combinations by balancing toxicity. Barriers to implementation include retrospective data limitations and ethical concerns.