Franco Pencle

Worthwhile read

Look at that, an innovation from our tiny island is pulling up a seat at a conversation for the refactoring and modernization of global health systems. No limits @FrancoPencle 👊🏿

We are running test pilots of our agents & so far they have excelled with co-authoring websites, books, marketing, handling customer service, doing research and presenting ideas. Unlike most, our agents are masters of every tool inside our toolkit and combined it is remarkable!

Always advocating for more unity amongst Jamaican entrepreneurs. We could take on the world together vs battling over the island’s pie. I know it’s a nice pie, but imagine what we could do.

A preprint from Johns Hopkins benchmarked 16 LLMs on correcting physician diagnostic errors. This is important work. While an LLM is only one component of a clinical AI system, it is arguably the most visible, and how we test that component shapes how health systems and regulators think about the whole. Arman Ouveysi, our Senior Medical Context Engineer, immediately noted something: the goal here is to evaluate whether LLMs can "effectively challenge, rather than merely confirm, an erroneous physician diagnosis." But the primary task prompt was "Do you agree with the physician diagnosis? Yes, or No. If No, suggest the most likely diagnosis." Agreement-framed prompts are well-documented for suppressing disagreement in LLMs (see reference 11 in the preprint itself). A prompt structured around confirming or denying a physician's conclusion is a difficult starting point for testing the capacity to independently challenge it. I am very interested in the disease-specific findings. Syphilis, spinal epidural abscess, and MI were missed consistently across all 16 models and this is absolutely worth exploring further. And the paper's conclusion is correct: clinical AI needs "adversarial, multi-agent workflows designed to prioritize skepticism over baseline agreement." No serious clinical AI deployment is a single-turn "do you agree?" query to a naked foundation model. Single models are never sufficient. Clinical AI needs systems where models challenge each other, where clinical knowledge comes from physician-authored guidelines rather than inferred from web text, and where specialized distilled models handle what generalist models handle poorly. But I love this type of work. Kudos. medrxiv.org/content/10.648…

Recently I was reminded that we need to take a moment to unplug and just be present in the moment, interface with the team and have conversation. I'm blessed to be surrounded by such great minds and look forward to what the future brings.

@StuartBlitz Elecare.Ai

Seems like there is so much admin burden in healthcare. I think I read it’s like 1/3 of the total spend. What if we used AI to cut down on scheduling, booking, billing, and operations? I’ll bet it would be pennies. Is anyone building this?

I'll be speaking via caribbeanaisummit.com on March 18th if you'd like to join our discussions to hear some of our thoughts & about our latest developments as well.

Ah mi boss.

It's safe to say we have produced one of the most capable agents available on the planet right now. It has handled coding, trading, accounting, legal, medical, logistics and misc business assignments in a way that has blown away everyone.

I am extremely proud to share that ElecareAl has been selected as part of #RegiónPlateada’s third cohort! We HAVE built one of the best Al-powered health system solutions that does not simply improve, it transforms care. We will make the most of the opportunity.

@Berci Elecare.ai

I’m proud to introduce 𝐓𝐡𝐞 𝐌𝐞𝐝𝐢𝐜𝐚𝐥 𝐅𝐮𝐭𝐮𝐫𝐢𝐬𝐭’𝐬 𝟏𝟎𝟎 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐇𝐞𝐚𝐥𝐭𝐡 𝐚𝐧𝐝 𝐀𝐈 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐨𝐟 𝟐𝟎𝟐𝟔! The reason to publish this list is simple: the digital health and AI fields are saturated with hype, and distinguishing the promising players from the noise is a challenge. The annual list is our way of cutting through the clamor to spotlight those we believe are genuinely making strides in this space. From startups through government authorities to prominent innovators, we look for key values: 1) A mindset for innovation 2) Truly disruptive technology 3) Viable business model 4) Clear dedication to digital health or healthcare AI 5) Active and transparent communication about their products 6) Peer-reviewed studies about their technology are optional, but represent a bonus A total of 23 companies from last year are no longer on the list. And here is the list of the newcomers: RingConn, RedDrop, YourBio, FusionVital, MediPredict, Whoop, Sidekick Health, Suki AI, Viz.ai, Qure.ai, Caresyntax, Moon Surgical, Turbine, Owkin, DeepScribe, Levels, BioIntelliSense, Kohler Health, Hyfe, Bioliberty, fundamental XR, Belong.life, and Eli.health. I also share three insights about the newcomers in the analysis. See the details here: medicalfuturist.com/the-medical-fu… Also, you can download and share the high-resolution version here: cdn.medicalfuturist.com/wp-content/upl…

As large scale investments take place for Ai in medicine is crucial that emerging markets not get left behind

February 11 is the day 🗳️ We’re red and readdyyyy🔥 Nomination Day: January 27. Lock in!

@JeffDean @ElecareAI would love to show what we've managed to do

An updated version of our MedGemma model is out with major accuracy improvements in medical-related tasks. We also released MedASR, which specialized for low error rates for medical speech recognition. We're excited to see what the health community does with these models! 🩺

app.elecare.ai has been built from the ground up to be a foundational Ai in Healthcare Technology, it combines a robust EHR with, billing, inventory management, crm and much more in one platform.

In the past few days we've seen ChatGPT health and now Claude has entered the space of Ai in health

2026 will see large scale adoption of Ai in Healthcare