Transforming Clinical Workflows with AI
How Large Language Models are moving from "hype" to "hospital infrastructure".
Medicine is fundamentally an information processing discipline. We gather signals (symptoms, labs, imaging), process them against a knowledge base (guidelines, experience), and output a decision (diagnosis, treatment).
Yet, for decades, the "operating system" of medicine has been stuck in the era of paper charts digitized into clunky EMRs. We spend up to 50% of our time on documentation, data entry, and hunting for information.
The Administrative Burden
The Reality
For every hour spent with a patient, a clinician spends nearly two hours on EHR and desk work. This is the primary driver of burnout.
Enter Large Language Models
LLMs offer a way to automate the "low-value" cognitive tasks that bog us down. We aren't asking AI to make life-or-death decisions; we are asking it to:
- Draft discharge summaries from daily progress notes.
- Structure unstructured text for research databases.
- Translate complex medical jargon into patient-friendly instructions.
Safety First: The Human in the Loop
Implementation must be rigorous. Hallucinations are not acceptable in a clinical setting. This is why my research focuses on RAG (Retrieval-Augmented Generation) and strict validation frameworks. We need AI that cites its sources.
Dr. med. Erdin Tokmak — Kardiologie & AI Research