Tim @TechTim08

@Berci The translational medicine comparison does a lot of work here. Fields move when they get a name people can repeat. Curious whether translational foresight catches the same way.

@MobiHealthNews A perfect benchmark score tells you the model passes the exam. It says little about Tuesday morning in a real clinic. Validation should start where the benchmark ends.

@Berci The useful sentence here is the split: the AI prepares, the clinician decides and signs. That division of labor seems like the version of AI primary care that can actually earn trust.

@MobiHealthNews Worth noticing how the doctor asks. He explains what the tool does and what it gives back to the patient, then asks. That explanation is doing the work, not the AI.

@MobiHealthNews Generating health content got cheap. Reviewing it did not. Interesting that the answer here is agents helping human reviewers instead of removing the sign-off.

@Berci Clearance measures a model at one moment. Deployment is where it actually lives. This gap is exactly why the push for post-market monitoring keeps getting louder.

@MobiHealthNews An AI tool is only as trusted as what it is built on. Wiring the answers to a reference doctors already use seems like a shortcut to that trust.

@Berci The interesting part is the labeling question. Once app content counts as drug labeling, the app is not a side project anymore. Curious if that pushes pharma to take these apps seriously or scares them off.

@MobiHealthNews Better data exchange could take some of the paperwork out of prior authorization. Curious whether insurers actually use it.

@EricTopol @LancetDigitalH The striking part is that this works from an ordinary photo. Health signals readable in everyday pictures is a consent question nobody has really answered yet.

@Berci Shaping beats predicting. Prediction is a spectator sport. The people building things get to vote on which future shows up.

@EricTopol @LindaGeddes The unsettling part is that a familiar name used to be the shortcut for trust. Once that is fakeable, every reader has to do the checking that journals used to do for them.

@EricTopol Interesting detail: the doctors rated their own autonomy support slightly lower. Patients walking in already informed changes the room. Not every doctor will love that.

@Berci The sandbox idea is the one I would steal. People learn tools by playing with fake data, not by reading another AI policy deck.

@EricTopol @NatureMedicine The part that stands out to me is drift. A model can be accurate at launch and quietly get worse as records and workflows change. Checking it once before deployment is not enough.

@Berci The ones that already work do boring things like booking and refills. Maybe that is the point. Admin work is where I would want one first.

@MobiHealthNews Booking was never the painful part. Finding out afterwards that the visit is not covered is. Checking that at the moment of booking sounds like the actual fix.

@Berci The draw itself seems like the easy part. The step after, turning a result into something you understand and know what to do with, feels like the harder one to move home.

@MobiHealthNews Who is expected to review the data between visits? A useful summary is one thing. Giving someone time to read it and a clear way to respond seems just as important.

@Berci Could each answer link back to the recording it came from? Being able to go from the chat to the actual data seems worth keeping, especially when sharing it with a doctor.