AMICO: A Semantic and Multimodal Framework for AI-Assisted Clinical Reporting

The increasing volume of unstructured clinical data - such as consultation audio, notes, and reports - has intensified the documentation burden on physicians, limiting time for direct patient interaction. Existing transcription and summarization systems remain largely proprietary and opaque, offering limited semantic interpretability or clinical control. We present AMICO (Anamnesi Medica Intelligente del Colloquio Ospedaliero), a multimodal framework for AI-assisted clinical documentation that integrates audio acquisition, semantic processing, and interactive human validation. The system combines automatic speech recognition, medical named entity recognition, summarization, and drug-drug interaction checking within a unified workflow designed to support, not replace, physician expertise. Evaluation shows that a fine-tuned LLM achieves superior accuracy and faithfulness compared to baseline LLMs, confirming the effectiveness of domain-specific adaptation for Italian medical dialogue summarization. AMICO demonstrates that moderately sized, transparent, and human-supervised models can meaningfully reduce documentation workload while ensuring clinical reliability and safety.