One thing that caught my attention recently is how AI is moving beyond simple note-taking and becoming part of actual clinical workflows.
In one case study, GeekyAnts modernized a dental platform by combining speech-to-text, Retrieval-Augmented Generation (RAG), and workflow redesign. Instead of only transcribing conversations, the system generated structured treatment plans, simplified doctor onboarding, and reportedly reduced onboarding completion time by 40% while improving treatment planning efficiency.
What I find interesting is that the biggest gains didn't seem to come from the LLM alone—they came from redesigning the workflow around it. AI handled repetitive documentation, while the application itself removed friction from legacy processes.
For developers building healthcare or enterprise software:
Where do you see the biggest ROI for AI today—documentation, decision support, or workflow automation?
How are you handling reliability and validation when using RAG in regulated environments?
I'd love to hear what approaches others are taking.
Top comments (0)