Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies

TL;DR - A structured survey plus thirteen case studies on using LLM-generated synthetic clinical communication (not tabular records) to train downstream healthcare NLP models where labeled real-world data is unavailable. It matters because authentic clinical dialogue is private, fragmented, and expensive to annotate, making synthetic data a practical bootstrap path.

• Frames clinical communication as distinct from structured records: meaning depends on speaker role, intent, causality, uncertainty, omission, and channel noise, so NLP must interpret information "as conveyed rather than coded."
• Survey is organized along four axes — source representation, communication form/participants, generation method, and downstream task — with LLMs converting records, diagnostic labels, symptom lists, or care plans into written and transcribed exchanges.
• Case studies cover EMS pre-arrival reports, field-radio casualty documentation, nurse handoffs, patient-portal triage, and low-resource discharge communication; fine-tuned encoder models were competitive with the evaluated zero-shot baselines, and deliberately degraded communication improved robustness.
• Key stated limitation: most work evaluates on held-out synthetic data, with little train-on-synthetic/test-on-authentic evidence; authentic-data transfer, safety, and external validation are needed before this becomes reusable clinical infrastructure.