arXiv:2606.18889v1 Announce Type: new Abstract: Text-based telemedicine increasingly relies on lightweight patient feedback, however, such feedback primarily reflects perceived communication quality rather than medical accuracy. We introduce an LM-guided counterfactual recommendation pipeline that discovers and refines interpretable communication features such as tone, personalization, actionability and completeness in addressing patient concerns, without interfering with the medical content. These features are used together with patient-doctor interaction metadata to estimate positive feedbac
The increasing reliance on telemedicine and lightweight patient feedback highlights the need for advanced AI tools to improve communication quality beyond basic medical accuracy.
This development indicates a growing application of AI, specifically large language models, to enhance nuanced aspects of human-computer interaction in critical sectors like healthcare, addressing subjective quality alongside objective correctness.
AI systems can now be guided to refine interpretable communication features like tone and completenss in medical interactions, moving beyond just content generation to qualitative improvement.
- · Telemedicine platforms
- · AI developers
- · Healthcare patients
- · Medical communication researchers
- · Providers relying solely on unrefined automated communications
- · Legacy patient feedback systems
Improved patient satisfaction and trust in AI-assisted medical communication.
Expansion of AI's role in qualitative rather than just quantitative data assessment across various service industries.
Potential for new ethical guidelines and regulatory frameworks around AI-driven communication refinement in sensitive fields.
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