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Innovating Cancer Care Through AI-Driven Biomarkers
- AI-based biomarkers derived from clinical data can personalize cancer treatment at scale and provide a cost-effective alternative to traditional molecular diagnostics.
- AI tools can analyze pathology, radiology, and electronic health record (EHR) data to detect microsatellite instability (MSI) and driver mutations such as EGFR, KRAS, and TP53.
- AI-based decision support systems can alleviate the workload of healthcare practitioners and automate tumor classification, as well as screening tools for clinical trials. However, widespread clinical adoption requires large-scale validation through clinical trials.
- The economic feasibility of implementing AI-driven solutions in diverse healthcare settings must be rigorously evaluated to ensure they do not exacerbate existing disparities.
- Addressing ethical dimensions like algorithmic fairness, inclusivity, and transparency are critical for building trust among clinicians and patients alike. Comprehensive guidelines and robust validation protocols are essential to mitigate the risks of over-relying on AI predictions.
- The integration of multimodal data, encompassing pathology, radiology, genomics, and EHR information, holds immense potential to enhance the accuracy of predictive biomarkers.
- Economic analyses of AI-based biomarkers highlight their potential to reduce the financial burden on healthcare systems by streamlining workflows and minimizing resource-intensive diagnostic procedures.
- As the field advances, AI-driven solutions are poised to redefine the standard of care in oncology, paving the way for a future where precision medicine is not a privilege but a standard accessible to all.
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