Reviewers largely see OpenAI as a strong, practical platform: they praise fast, reliable models, solid APIs, easy integration, and broad use across coding, writing, image generation, voice, search, and agent workflows. Makers of , , and say their products run core features on OpenAI models. The main complaints are familiar: high or hard-to-predict costs, context and memory limits, occasional generic or off-target answers, version churn, rate limits, and a desire for more transparency around updates.
Mom Clock
the "connect your medical records" part is the piece I'd want more detail on before trying it. is health data handled under a separate, more restricted data policy than regular chat data (no training on it, stricter retention), or is it treated the same as any other conversation once it's in ChatGPT? that distinction matters a lot more here than with a typical launch.
I run a GLP-1 information community, and the number of people arriving with AI-generated health summaries has exploded this year - some genuinely helpful, some confidently wrong about dosing or insurance rules. So the real question for Health in ChatGPT is how it handles uncertainty: citing sources people can verify, and knowing when to say “ask your doctor”. That matters more in this domain than any other.
This is definitely the future, but instead of "staying in control of what you share", health data should be never be shared by default as a company policy.
Refocus
The "prepare for appointments" piece stands out to me. Building voice AI for aging-in-place with Callie Care, I keep seeing how much context gets lost between visits, especially with older parents who forget what the doctor actually said. Are you thinking about a caregiver or family view, so an adult child could help manage a parent's health info without taking over their whole account?