arXiv:2604.10783v2 Announce Type: replace-cross Abstract: Designing reward functions for reinforcement learning (RL) in healthcare remains challenging because clinically meaningful outcomes are sparse, delayed, and difficult to explicitly specify. Although structured clinical data capture physiologic states, they often fail to reflect broader aspects of patient trajectories such as treatment response, recovery dynamics, and intervention burden. Clinical narratives, by contrast, encode longitudinal clinician assessments of disease progression, treatment effectiveness, and recovery, providing a
The proliferation of digital clinical data and advancements in large language models make it increasingly feasible to extract nuanced insights from unstructured clinical narratives for AI applications in healthcare.
This research provides a pathway to develop more human-centric and clinically aligned AI models for complex medical decisions by incorporating qualitative clinical judgments often missed by structured data.
AI-driven treatment recommendations in healthcare can now move beyond purely quantitative metrics to include qualitative assessments of patient well-being and recovery dynamics, leading to more personalized and effective interventions.
- · AI healthcare developers
- · Patients with complex conditions
- · Clinical researchers
- · Hospitals adopting AI-driven protocols
- · Traditional rule-based clinical decision support systems
- · AI models relying solely on structured data
Improved sepsis treatment outcomes through AI-driven personalized healthcare.
Accelerated development of AI agents capable of interpreting and synthesizing complex medical texts for nuanced decision-making.
Ethical and regulatory frameworks will need to evolve rapidly to incorporate AI systems that derive treatment objectives from subjective clinical narratives.
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Read at arXiv cs.LG