Precision Recall Controllable Radiology Report Generation via Hybrid Natural Language and Clinical Reward Learning
arXiv:2606.21447v2 Announce Type: replace Abstract: Automated radiology report generation (RRG) has gained increasing attention because it can reduce the heavy workload of clinical report writing. However, most existing methods mainly optimize for natural language generation (NLG) metrics that focus on language fluency, while providing little control over clinically important factors such as precision and recall. As consequence, generated reports may be fluent but not well aligned with different clinical needs. To address this challenge, we propose a reinforcement learning framework for precis
The increasing sophistication of natural language generation models enables finer control over outputs, making techniques like reinforcement learning for specific clinical metrics viable now.
This development addresses a critical limitation in AI-generated medical reports, moving beyond mere fluency to medically actionable and reliable outputs, which is essential for adoption in healthcare.
AI tools can now be optimized for clinical utility (precision/recall) rather than just linguistic fluency, enhancing trustworthiness and practical application in sensitive domains like radiology.
- · Healthcare AI developers
- · Radiologists
- · Hospitals
- · Patients
- · AI models focused solely on NLG metrics
More accurate and clinically relevant AI-generated radiology reports will become available.
Increased adoption of AI in medical diagnostics due to higher reliability, potentially reducing diagnostic errors and improving patient outcomes.
The development of similar clinical utility optimization frameworks could extend to other medical specialties and critical applications beyond radiology.
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Read at arXiv cs.CL