arXiv:2605.30119v1 Announce Type: new Abstract: Survival analysis concerns the task of predicting the time until an event occurs. Often used in the medical field, survival analysis deals with incomplete (i.e., censored) data, for instance, from patients who did not experience the event during the duration of the study. For practical use, both accuracy and interpretability are important. Survival trees are easy-to-follow survival models that split the patient cohort recursively into discrete patient groups. Whilst survival trees can capture complex relationships, they typically need to grow lar
The continuous advancements in AI and machine learning techniques, particularly in explainability and interpretability, are driving innovation in critical applications like medicine where transparent decision-making is paramount.
This research advances interpretable AI in high-stakes fields like healthcare, offering methods that balance predictive accuracy with the crucial need for human-understandable survival models.
The ability to generate more interpretable, yet accurate, survival models through techniques like evolving features or entire trees could increase adoption and trust in AI-driven medical prognostics.
- · Medical AI developers
- · Healthcare providers
- · Patients needing prognoses
- · Regulatory bodies
- · Black-box AI models in healthcare
- · Traditional statistical methods for survival analysis
Improved interpretability of AI predictions in medical survival analysis will lead to greater clinical acceptance and application.
Increased trust and adoption of these models could accelerate personalized medicine initiatives and precision treatment strategies.
Ethical and regulatory frameworks for AI in healthcare may evolve to prioritize and mandate specific levels of model interpretability for patient-facing applications.
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