Personalized Therapies and AI: Decision Algorithms for Optimal Oncological Treatments in Precision Oncology

Precision oncology has revolutionized cancer treatment approaches, enabling physicians to tailor oncological therapies to the individual characteristics of each patient. In this context, artificial intelligence (AI) has emerged as a powerful tool to enhance response prediction and optimize the selection of targeted treatments. The ability of decision algorithms to analyze large volumes of data and extract complex patterns is transforming clinical practice, offering new opportunities for personalizing cancer treatment.

Diving Deeper into AI and Personalized Therapies

The integration of AI in oncology has facilitated significant advancements in identifying biomarkers and predicting treatment responses. For instance, a recent study demonstrated how machine learning can predict responses to image-guided therapies in hepatocellular carcinoma, improving patient selection for specific treatments. Additionally, AI has been utilized to develop models that predict the efficacy of antineoplastic strategies, as described in a recent article, underscoring its potential in precision oncology.

Another notable example is the use of machine learning models to guide adjuvant treatment in head and neck cancer, identifying patients who may benefit from chemoradiation. These models not only enhance the precision in treatment selection but also optimize the use of clinical resources by avoiding unnecessary therapies in patients who would not benefit from them.

Conclusions

The application of AI in oncology is redefining the cancer treatment paradigm, enabling a more effective and personalized precision oncology. AI-based decision algorithms are proving to be valuable tools for improving response prediction and the selection of targeted treatments, translating into better outcomes for patients. As we continue to explore and develop these technologies, it is crucial for healthcare professionals to stay informed about the advancements and challenges in this rapidly evolving field.

Referencias

• [1] Using Machine Learning to Predict Response to Image-guided Therapies for Hepatocellular Carcinoma
• [2] Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy
• [3] Machine Learning-Guided Adjuvant Treatment of Head and Neck Cancer

Created 20/1/2025