**3.6 Impact on clinical decision-making support toward precision medicine in radiation oncology**

ML tools for computer-aided detection/diagnosis [15–22] as "second opinion" systems for clinical decision-making support would undoubtedly enhance the radiologists' performance and hence improved diagnostic performance. The emerging paradigms in radiomics for therapeutic outcome predictions (i.e., patient's survival, decrease recurrence, late complication, etc.) [97–102] for individual patients would maximize its potential impact on precision radiotherapy. Individual patients can be stratified into subtypes based on radiomic biomarkers that contain information about cancer traits that determine the patient's prognosis [97]. Therefore, physicians may prescribe a more or less intense radiation regimen for an individual based on model predictions of local control benefit and toxicity risk [2], which would be considered for the optimal treatment planning design process and hence improving the quality of life for radiotherapy cancer patients. Effective implementation of adaptive radiation therapy with ML [85–88] can also further improve the precision in the radiotherapy treatments. The pre-planning prediction of dosimetric tradeoffs to assist physicians and patients

to make better informed decisions about treatment modality and dose prescription [68] thus it can establish individualized and achievable goals. The clinical implications derived from personalized cancer therapy ensure not only that patients receive optimal treatment, but also that the right resources are being used for the right patients.
