With Dr. Joseph Kvedar and Dr. Daniel Polsky, we published “Policy brief: ambient AI scribes and the coding arms race” in
@natureportfolio.nature.com Digital Medicine on early signals scribes can raise coding intensity:
www.nature.com/articles/s41...

Special thanks to our speakers:
Natalia Trayanova
@mdredze.bsky.social
@alexisbattle.bsky.social
@tinglongdai.com
Haris Sair
Alex Baras
Rick Carrick
Robert Stevens
Ashley Kieman
Agbessi Amouzou

Thank you for spotlighting our study!

Read the full study (open access):
🔗 nature.com/articles/s41...

Grateful to work with a dream team:

Drs. Haiyang Yang, Risa Wolf, Nestoras Mathioudakis, & Amy Knight @jhu.edu @hopkinsmedicine.bsky.social
plus Dr. Yuna Nakayasu, my former MBA/MPH student @johnshopkinssph.bsky.social @jhu.edu, now of McKinsey

Yet, clinicians also saw promise:

Belief that GenAI improves accuracy: 4.30

Institution-customized GenAI viewed even more favorably: 4.96

(7-point scale)

Ratings of the care experience also dropped — from 4.48 ⭐️ to 3.08 ⭐️ (5-star scale).

Framing GenAI as a verification tool helped (clinical skill 4.99, competence 4.94), but the gap remained.

The numbers are striking.

In our randomized experiment of 276 clinicians, a physician who used GenAI as a decision aid tool was rated far lower:

Clinical skill: 3.79 vs 5.93

Overall competence: 3.71 vs 5.99

(7-point scale)

Medical AI is racing ahead; @fda.gov S_FDA oversight must keep pace.

Read the full @ai.nejm.org study here ➡️

bit.ly/fdaai25

Grateful to Branden Lee, Shivam Patel, CrystalFavorito, Sara Sandri, and Maria Rain Jennings, first- and second-year @jhu.edu medical students already shaping medical AI.

Thanks also to Drs Charlotte Haug and Isaac Kohane of @ai.nejm.org for thoughtful guidance.

(6/7)

Safety gap

@fda.gov-cleared AI devices from publicly traded firms are recalled far more often: up to 30 × compared with those from private firms (14.4% vs 1.3% of cleared devices)

Development and commercialization models corrects with patient risk and should guide oversight.

(5/7)

Tech under the hood

Deep learning now powers half of new @fda.gov-cleared devices.

Transparency is improving, yet 62% of all devices still give little or no detail about how their AI works.

(4/7)

AI Clearance curve

Average @fda.gov clearances jumped from 1.4 devices per year in 1995-2014 to 146 per year in 2020-24—a 100-fold surge.

Total count went from 27 in the first 20 years to 729 in the last five.

In-house development drives nearly all growth.

(3/7)

Who is building what?

69% of @fda.gov-cleared AI device manufacturers are private, but public firms make more devices per company.

General Radiology leads with 32%, followed by cardiovascular (18%) and neuropsychiatry (15%).

A booming yet scattered market.

(2/7)

"It's not just about higher costs -- it's about readiness." -- @tinglongdai.com, of @jhu.edu, on the threat that tariffs pose to #publichealth preparedness.
www.medpagetoday.com/special-repo...

5/5 As a top exporter leading in diagnostic and lab reagents, a prolonged trade war could render the global scientific supply chain more fragile, costly, and unreliable.​

We may be at the onset of a tariff-induced chaos period.​