Join Heidi Health and AWS's Generative AI Innovation Center (GenAIIC) for a behind-the-scenes look at building and deploying custom speech-to-text AI for healthcare. Learn hard-won lessons and a practical blueprint: curating domain-specific training data, fine-tuning open-weight models, validating non-deterministic outputs at scale, and shipping to production with optimized inference. Both teams share how AWS services reduced infrastructure complexity, accelerated iteration cycles, and scaled custom models across diverse real-world use cases — all while maintaining security and cost efficiency. Ideal for ML engineers, data scientists, and technical leaders exploring fine-tuning and production ML on AWS.
What this session is about
Playbook
Editorial commentary · what to actually do about this on Monday
The concept
Fine-tuning open-weight speech-to-text models for healthcare. Domain-specific training data, validating non-deterministic outputs at scale, optimised inference.
Why it matters
Generic STT misses medical terminology. Fine-tuning closes the gap; it's a textbook fine-tune use case.
The hard parts
Validating non-deterministic STT output at scale is statistical, not anecdotal. You need a test set, not vibes.
Playbook moves
(1) Build a labelled gold-standard test set per medical specialty. (2) Re-evaluate monthly. (3) Track word-error rate by accent, specialty, and recording quality separately.
The surprise
The dominant accuracy issue in healthcare STT in Australia isn't medical jargon — it's *accents and code-switching*. Patient cohorts are linguistically diverse; clinicians switch registers. Train accordingly; English-only test sets miss most of the failure cases.
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Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.
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