arXiv:2601.09696v2 Announce Type: replace Abstract: LLMs are increasingly being integrated into clinical workflows, yet they often lack clinical empathy, an essential aspect of effective doctor-patient communication. Existing NLP frameworks focus on reactively labeling empathy in doctors' responses but offer limited support for anticipatory modeling of empathy needs, especially in general health queries. We introduce the Empathy Applicability Framework (EAF), a theory-driven approach that classifies patient queries in terms of the applicability of emotional reactions and interpretations, based
The rapid integration of LLMs into critical sectors like healthcare, coupled with their recognized limitations in nuanced human interaction, necessitates immediate solutions for effective and ethical deployment.
This development addresses a key ethical and practical challenge in AI adoption within healthcare, enhancing patient trust and the efficacy of AI-driven clinical support.
The explicit classification of patient queries based on empathy applicability allows for more targeted and appropriate AI responses rather than generic clinical interactions.
- · AI healthcare providers
- · Patients
- · Natural Language Processing researchers
- · Generic LLM providers
Improved patient satisfaction and trust in AI-driven healthcare communications.
Accelerated development of specialized, empathetically aware AI models for various clinical applications.
Potential for new regulatory frameworks around AI 'empathy' and ethical interaction in sensitive fields.
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