arXiv:2506.02075v2 Announce Type: replace-cross Abstract: The current state of evaluation in survival analysis is plagued by the persistent use of evaluation metrics in ways that are misaligned with the stated modeling objective. In addition, many such evaluations are based on censoring assumptions that are left implicit or unjustified. This means that the reported performance can be misleading and may fail to answer the scientific or modeling question the evaluation was intended to address. In this position paper, we critically examine evaluation practices in survival analysis and highlight h
The proliferation of AI/ML in critical applications like healthcare (survival analysis) is spotlighting the need for more robust and context-aware evaluation methodologies.
Incorrect or misleading evaluation of AI models, particularly in high-stakes fields, can lead to flawed deployments, poor decision-making, and erosion of trust in AI systems.
There will be a renewed focus on tailoring evaluation metrics to specific modeling objectives and explicit consideration of underlying assumptions in survival analysis, improving the reliability of AI applications in health and other fields.
- · AI model developers with rigorous evaluation practices
- · Healthcare providers relying on survival analysis
- · Patients whose treatments are informed by these models
- · Developers solely relying on simplistic C-index metrics
- · Systems using poorly validated AI models for critical decisions
Improved model trustworthiness and more accurate predictions in survival analysis.
Increased adoption of more sophisticated, context-specific evaluation frameworks across AI/ML domains beyond survival analysis.
Potential for new academic and industry standards to emerge for AI model validation, impacting regulatory frameworks and market competition.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG