Latest / Best of LinkedIn: Digital Transformation & Tech / Best of LinkedIn: Health Tech CW 46/ 47

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Best of LinkedIn: Health Tech CW 46/ 47 · Best of LinkedIn: Digital Transformation & Tech

• 0:00
  Speaker 0: This deep dive is provided by Thomas Allgaier and Frennis, based on the most relevant LinkedIn posts on health tech in CW-Forty-Six and Forty-Seven.
• 0:08
  Speaker 0: Frennis equips product and strategy teams with market and competitive intelligence to navigate the health tech landscape.
• 0:15
  Speaker 0: Welcome back!
• 0:16
  Speaker 0: Okay, let's just jump right in.
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  Speaker 0: We spent the last couple of weeks tracking the big health tech trends on LinkedIn, specifically weeks, forty six and forty seven.
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  Speaker 0: Right.
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  Speaker 0: And what was really striking was this this palpable shift in the conversation.
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  Speaker 0: It feels like we're moving past the future hype and we're getting into the nuts and bolts of real embedded clinical tools that are actually changing how things are done today.
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  Speaker 1: That's exactly it.
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  Speaker 1: The mission here is to cut through all that noise.
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  Speaker 1: And we're seeing AI move decisively out of these limited pilots.
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  Speaker 1: into products you can actually deploy.
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  Speaker 1: But that forces you to look at the foundations, things like cloud, data standards, security.
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  Speaker 1: Because without that bedrock, the AI just can't scale.
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  Speaker 0: And it's not just the tech, right?
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  Speaker 0: It's the human element.
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  Speaker 0: We saw so much about how innovation is widening access to care, which is
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  Speaker 1: great.
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  Speaker 0: But at the same time, it's introducing some really serious new questions about clinical rigor and patient safety.
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  Speaker 0: It's like a complex picture.
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  Speaker 1: It is.
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  Speaker 1: So let's start where the change is happening fastest.
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  Speaker 1: The core of diagnosis.
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  Speaker 1: OK.
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  Speaker 1: AI is just fundamentally reshaping radiology.
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  Speaker 1: And the big step now is what they're calling multimodal integration.
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  Speaker 0: So not just looking at one image in isolation.
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  Speaker 1: Exactly.
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  Speaker 1: As Matthias Goyen noted, the future of precision medicine isn't going to come from a scan alone.
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  Speaker 1: You have to combine that imaging with pathology, with genomics, with the whole clinical context.
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  Speaker 1: And that
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  Speaker 0: fusion is getting incredibly specific.
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  Speaker 0: I saw a perfect example of this.
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  Speaker 0: Yoel Baca is highlighted to study on prostate cancer.
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  Speaker 0: They show that when you combine PSMA-PET imaging with AI analysis, it just drastically enhances how you characterize lesions.
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  Speaker 1: Right, and predict patient outcomes.
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  Speaker 1: It's a huge leap over just using the imaging by itself.
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  Speaker 0: So you're getting sharper, earlier insights that a clinician can actually act on.
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  Speaker 1: But to do To handle all that data in real time, you need a massive performance leap in the hardware.
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  Speaker 0: And we saw an answer for that.
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  Speaker 0: TheodoraSaz reported on a breakthrough from Philips and NVIDIA.
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  Speaker 1: With the GPUs?
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  Speaker 0: Yes, using GPU tech to accelerate ultrasound beamforming up to ten times faster with zero loss in image quality.
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  Speaker 0: Ten
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  Speaker 1: times?
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  Speaker 1: I mean, that's a game changer in a busy hospital.
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  Speaker 1: That's not a lab improvement.
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  Speaker 1: That's an operational benefit today.
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  Speaker 0: And the speed has these other impacts I found fascinating.
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  Speaker 0: And hey, Helwitz pointed out that AI acceleration in MRI, like advanced denoising.
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  Speaker 1: Which shortens the scan time.
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  Speaker 0: Exactly.
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  Speaker 0: That directly minimizes the energy footprint of these really high energy systems.
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  Speaker 0: So you're connecting clinical efficiency straight to sustainability.
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  Speaker 0: That's
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  Speaker 1: a perfect example of optimization.
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  Speaker 1: And we also saw this maturing in really specific areas like neuroimaging.
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  Speaker 1: Oh,
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  Speaker 0: yeah.
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  Speaker 1: Luis Cuevas announced Phillips' extended partnership with Cortex.ai.
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  Speaker 1: They're integrating.
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  Speaker 1: AI neuroanalytics directly into the MR systems themselves.
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  Speaker 0: Which means you're moving from a doctor's subjective interpretation of a brain scan to objective quantitative data much faster.
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  Speaker 1: Exactly.
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  Speaker 1: Critical for tracking conditions like dementia over time.
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  Speaker 0: So moving from diagnosis into actual treatment, the conversation got really focused on a high precision intervention.
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  Speaker 0: And the big headliner was digital twins.
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  Speaker 1: And this isn't science fiction anymore.
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  Speaker 0: No, it's being positioned as a really practical bridge from R&D to treating individual patients.
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  Speaker 1: Alina Schvitz had a great post clarifying this.
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  Speaker 1: A digital twin isn't just a scan.
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  Speaker 1: It's a full virtual replica of a patient.
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  Speaker 1: Built
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  Speaker 0: from what?
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  Speaker 0: EHRs?
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  Speaker 0: Genetics?
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  Speaker 1: Everything.
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  Speaker 1: EHR, scans, genetics, wearable data, you put it all together to create these incredibly predictive models.
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  Speaker 0: And they're being used now to simulate drug trials for things like Alzheimer's, which cuts down the sample size needed.
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  Speaker 1: Which is massive.
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  Speaker 1: Think of the time, the cost, the reduction in patient risk, and in surgery, the value is even more immediate.
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  Speaker 1: Right.
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  Speaker 1: Matthew Juliano from Medtronic presented their pipeline for robotic surgery, digital twins.
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  Speaker 1: You use it for capture, playback, training.
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  Speaker 0: So it's basically a flight simulator for a surgeon.
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  Speaker 1: That's a perfect way to put it.
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  Speaker 1: It pairs the robot's precision with simulation to improve safety before you even make an incision.
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  Speaker 1: Newen P called it a force multiplier.
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  Speaker 0: A force multiplier.
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  Speaker 0: I like that.
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  Speaker 1: Yeah, you're using AI and augmented reality to give the surgeon this real-time, three-D anatomical overlay.
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  Speaker 0: Like giving them X revision.
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  Speaker 1: Basically.
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  Speaker 1: And we have concrete examples of this in the field now.
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  Speaker 1: Mark Stauffles and Ruben Olivier highlighted a collaboration between Phillips and Edwards Life Sciences.
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  Speaker 1: On
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  Speaker 0: that hashtag device guide solution.
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  Speaker 0: Yes.
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  Speaker 1: It's designed to simplify a really complex, minimally invasive heart procedure mitral valve repair.
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  Speaker 1: It turns confusing two-D imaging into intuitive three-D guidance for the surgical team.
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  Speaker 0: Which is a huge safety boost for something that requires incredible precision.
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  Speaker 1: But all this amazing tech is only good if people actually use it.
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  Speaker 1: And that means supporting the human workflow, not disrupting it.
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  Speaker 0: He said healthcare needs tech that disappears so compassion can take the front seat.
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  Speaker 1: Right.
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  Speaker 1: If the technology is louder than the patient, you've failed.
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  Speaker 1: So
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  Speaker 0: let's unpack that.
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  Speaker 0: I mean, think about burnout.
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  Speaker 0: The most practical use case we saw was ambient AI scribes.
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  Speaker 0: Krista Calpe showed a study showing that using these AI scribes dropped physician burnout from over fifty percent down to about thirty eight percent.
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  Speaker 0: That's a
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  Speaker 1: huge drop.
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  Speaker 1: The ROI there isn't just saving time on notes, it's keeping your doctors from quitting.
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  Speaker 0: And that gets to the core of adoption.
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  Speaker 0: Sathvik Billakanti pointed this out with that famous
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  Speaker 1: story.
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  Speaker 1: Multi-million dollar AI system versus the WhatsApp
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  Speaker 0: group.
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  Speaker 0: Yeah.
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  Speaker 0: Why did the WhatsApp group win?
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  Speaker 0: Because the tech team built a huge system, but the nurses just needed to solve one daily frustration.
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  Speaker 0: It's
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  Speaker 1: a perfect lesson.
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  Speaker 1: The tech that wins just makes the human better at their job.
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  Speaker 1: And big tech is clearly paying attention.
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  Speaker 1: They
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  Speaker 0: are.
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  Speaker 0: Oracle Health is embedding AI deep into the EHR for note automation.
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  Speaker 1: And Carl Nislow noted that OpenAI is exploring a personal health assistant.
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  Speaker 1: I mean, they have eight hundred million weekly users already asking chat GPT health questions.
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  Speaker 1: Wow.
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  Speaker 0: Shifting gears a bit to access.
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  Speaker 0: We saw some powerful examples of tech closing huge equity gaps.
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  Speaker 0: Jantomar highlighted India's labs on wheels, mobile health vans using IOMT and FiveG to bring diagnostics right into remote villages.
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  Speaker 1: And we're seeing that connectivity finally reached the most remote areas here, too.
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  Speaker 1: Joel Ibarthelamy gave examples of how digital health is transforming indigenous care in North America.
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  Speaker 1: How?
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  Speaker 1: Low Earth orbit satellites.
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  Speaker 1: Well, ELUS are providing the broadband that traditional infrastructure just couldn't deliver.
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  Speaker 0: But that access creates new problems.
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  Speaker 0: We saw a really sharp debate around the new GOP-I weight loss drugs.
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  Speaker 1: Right.
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  Speaker 1: Michelle Davy noted how telemedicine is expanding access, which seems good.
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  Speaker 0: But then Nisha Chellam offered a really strong warning.
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  Speaker 0: She called this democratization a double-edged sword.
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  Speaker 0: Because
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  Speaker 1: you're dispensing powerful drugs transactionally without the comprehensive care and monitoring that you go with them.
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  Speaker 0: Exactly.
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  Speaker 0: You risk serious complications and the drugs don't even work as well over time.
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  Speaker 0: The innovation and access has to be matched with clinical responsibility.
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  Speaker 0: Which
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  Speaker 1: brings us right to the biggest challenge of all.
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  Speaker 0: Why do so many of these heavily funded health tech startups just...
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  Speaker 1: Well, Reza Hosseini-Gomi, who's been building these companies for fifteen years, he laid out the pattern.
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  Speaker 1: They fail because they skip the hard parts.
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  Speaker 1: They don't secure trust, they don't integrate properly, and they can't figure out reimbursement or date equality.
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  Speaker 0: So he's saying healthcare is a trust problem wrapped in regulation.
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  Speaker 0: It's not just a tech problem.
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  Speaker 1: Exactly.
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  Speaker 1: You look at the big failures.
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  Speaker 1: Forward health, all of AI, Babylon.
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  Speaker 1: They all prioritize disruption over integration.
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  Speaker 1: Segerberg van Roysen argued that AI won't revolutionize anything until we fix these structural issues.
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  Speaker 0: Like siloed data and poor usability.
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  Speaker 1: In a really unclear ROI, who actually pays for the efficiency gains?
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  Speaker 1: That's a huge question.
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  Speaker 0: These financial pressures are also changing how hospitals even think about equipment.
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  Speaker 0: Kinesh Kamath described it as the uberola moment for healthcare.
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  Speaker 1: The move to devices as service or DAT.
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  Speaker 0: Right.
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  Speaker 0: Why own a rapidly depreciating high-tech machine when you can just subscribe to the service it provides?
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  Speaker 1: It's a massive shift from CAPEX to OPEX.
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  Speaker 1: Ownership is becoming a liability.
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  Speaker 0: And Paul Reckmans from Phillips noted that ninety-six percent of healthcare leaders are facing financial challenges.
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  Speaker 0: Almost sixty percent have had to delay or cancel investments in new tech.
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  Speaker 1: So the capital just isn't there.
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  Speaker 1: Which means you have to fix the foundations first.
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  Speaker 1: A CMK emphasized that open standards like FHIR are non-negotiable for AI to work at scale.
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  Speaker 0: And we also saw some good news on the regulatory side in genomics.
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  Speaker 0: We
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  Speaker 1: did.
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  Speaker 1: Lisa Gurry celebrated the FDA's new plausible mechanism pathway.
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  Speaker 0: Which is designed to speed up access to personalized gene therapies.
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  Speaker 0: That's a huge deal.
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  Speaker 0: It shows policies trying to adapt to the science.
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  Speaker 1: It does.
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  Speaker 0: So when you look across all of this, from diagnostics to finance, what really stands out to me is that the tech that works is the tech that empowers the human.
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  Speaker 0: It's not about replacing them.
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  Speaker 0: It's about ambient scribes reducing burnout or surgical copilots enhancing precision.
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  Speaker 0: It's all about augmentation.
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  Speaker 1: Absolutely.
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  Speaker 1: The technology that succeeds is the one that makes the clinician better at their core job, which is compassion and care.
• 9:37
  Speaker 0: If you enjoyed this deep dive, new episodes drop every two weeks.
• 9:40
  Speaker 0: Also check out our other editions on ICT and tech insights, defense tech, cloud, digital products and services, artificial intelligence and sustainability in green ICT.
• 9:49
  Speaker 1: And as a final thought, we have to look beyond all this shiny new tech.
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  Speaker 1: Simon Philip Ross shared some OECD data that was, well, pretty striking.
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  Speaker 1: What
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  Speaker 0: was it?
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  Speaker 1: Only three percent of all health spending goes into prevention.
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  Speaker 1: and just fourteen percent into primary care.
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  Speaker 1: We were overwhelmingly funding sickness.
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  Speaker 1: Meanwhile, Vicky Britton noted that health literacy is stuck in the paper era.
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  Speaker 1: Patients are navigating Reddit and TikTok for medical advice, sometimes out of sheer frustration with the system.
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  Speaker 1: The real revolution won't just be an algorithm.
• 10:21
  Speaker 1: It'll be rewiring budgets to fund prevention and giving people the digital literacy they need to handle all this information responsibly.
• 10:27
  Speaker 0: Thank you for diving deep with us.
• 10:29
  Speaker 0: That is certainly something to think about.
• 10:31
  Speaker 0: We look forward to seeing you next time and don't forget to subscribe.