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Best of LinkedIn: Health Tech CW 04/ 05 · Best of LinkedIn: Digital Transformation & Tech

• 0:00
  Speaker 1: This episode is provided by Thomas Allgeier and Frennis based on the most relevant LinkedIn posts on health tech in CW four and five.
• 0:08
  Speaker 1: Frennis equips product and strategy teams with market and competitive intelligence to navigate the health tech landscape.
• 0:14
  Speaker 0: It is great to be back and you know looking at the intelligence stack from calendar weeks four and five there's a definite shift in the air.
• 0:22
  Speaker 0: It feels
• 0:22
  Speaker 1: heavier doesn't it?
• 0:23
  Speaker 1: Less about the shiny object and more about well the heavy lifting.
• 0:27
  Speaker 0: Precisely.
• 0:28
  Speaker 0: If last year was the year of the demo, where everyone was showing off what was theoretically possible, the start of twenty.
• 0:34
  Speaker 0: twenty-six is really shaping up to be the era of the execution frontier.
• 0:38
  Speaker 1: The industry is rolling up its sleeves.
• 0:40
  Speaker 0: It is.
• 0:41
  Speaker 0: We're seeing this clear shift from, you know, look what this AI can do to, does this actually work when things get messy in a real hospital?
• 0:48
  Speaker 1: And that's the mission for this deep dive.
• 0:50
  Speaker 1: We're stripping away the hype to see what's actually sticking.
• 0:52
  Speaker 1: We've got what, four main clusters to get through?
• 0:55
  Speaker 0: Yep.
• 0:55
  Speaker 0: First, we'll dissect the reality gap in AI diagnostics, specifically why these magic demos are failing in clinical practice.
• 1:02
  Speaker 1: Then we're scrubbing into the operating room.
• 1:04
  Speaker 0: Right.
• 1:04
  Speaker 0: Looking at why robotics is moving from a hardware conversation to a workflow conversation.
• 1:10
  Speaker 1: We'll also take a hard look at the consumer side wearables, glucose monitoring, and the harsh truth that, you know... data doesn't equal behavior change.
• 1:19
  Speaker 0: And finally, we have to talk about the energy vampires and strategy.
• 1:23
  Speaker 0: We'll look at why pilot programs are failing and a massive regulatory blind spot that was frankly exposed this week.
• 1:31
  Speaker 1: It's a dense stack.
• 1:32
  Speaker 1: So let's get right into it.
• 1:33
  Speaker 1: Theme one, AI and care delivery.
• 1:36
  Speaker 1: And right away, there is this This real tension between marketing promises and clinical reality.
• 1:42
  Speaker 0: It's the network to product gap.
• 1:45
  Speaker 0: And nothing showed this better this week than a story shared by Yongcha, the former Apple Health exec.
• 1:50
  Speaker 0: Oh,
• 1:50
  Speaker 1: I saw this one.
• 1:51
  Speaker 1: He highlighted
• 1:51
  Speaker 0: a really telling experiment and involved a reporter from the Washington Post and Chad GPT Health.
• 1:56
  Speaker 1: This is the one where the reporter fed ten years of his Apple Watch data into the system, right?
• 2:00
  Speaker 1: Correct.
• 2:01
  Speaker 0: Ten years of heart rate, sleep, activity data.
• 2:04
  Speaker 0: And he asked the AI a simple question.
• 2:06
  Speaker 0: grade my cardiac health.
• 2:08
  Speaker 0: The AI came back and gave him F.
• 2:09
  Speaker 1: An F?
• 2:10
  Speaker 1: Wow.
• 2:11
  Speaker 1: That implies imminent failure.
• 2:13
  Speaker 1: Which
• 2:13
  Speaker 0: is terrifying.
• 2:14
  Speaker 0: Especially since his actual human cardiologist had told him he was perfectly fine.
• 2:18
  Speaker 1: Okay, but here's
• 2:19
  Speaker 0: the kicker.
• 2:19
  Speaker 0: Here's the kicker.
• 2:20
  Speaker 0: The part that really matters for our listeners building these tools.
• 2:24
  Speaker 0: The reporter asked the AI again.
• 2:27
  Speaker 0: Same data.
• 2:28
  Speaker 0: This time.
• 2:28
  Speaker 0: It gave him a C.
• 2:29
  Speaker 1: A C?
• 2:30
  Speaker 1: He asked
• 2:30
  Speaker 0: a third time.
• 2:31
  Speaker 0: It gave him a B.
• 2:31
  Speaker 1: Hold on.
• 2:32
  Speaker 1: Same inputs.
• 2:33
  Speaker 1: Three wildly different outputs ranging from you're failing to you're doing great.
• 2:38
  Speaker 1: That's not a diagnostic tool.
• 2:39
  Speaker 1: That's that's a random number generator.
• 2:42
  Speaker 0: That is exactly Young Chae's point.
• 2:44
  Speaker 0: He argues that general purpose large language models lack what he calls native priors for trend estimation.
• 2:50
  Speaker 1: Unpack that for us native priors.
• 2:52
  Speaker 0: Think of it as context or a baseline.
• 2:55
  Speaker 0: A human doctor has a prior understanding of what's a normal fluctuation versus a dangerous anomaly for you based on your history.
• 3:02
  Speaker 0: Okay.
• 3:02
  Speaker 0: The LLM is just pattern matching against a huge generic training set.
• 3:06
  Speaker 0: Doesn't know you, it's guessing.
• 3:08
  Speaker 1: So, Charles basically saying we have a reliability crisis.
• 3:12
  Speaker 1: It's easy to make a demo that gets the right answer once for an investor deck.
• 3:16
  Speaker 0: But it's incredibly hard to build a product that's reliable enough to trust with the diagnosis.
• 3:21
  Speaker 0: Reliability is the currency.
• 3:23
  Speaker 1: And if an AI tells a healthy patient they're dying, that's not just an error, it's actual harm.
• 3:29
  Speaker 0: It is.
• 3:29
  Speaker 0: It creates anxiety.
• 3:30
  Speaker 0: It wastes clinical resources and it erodes trust.
• 3:34
  Speaker 0: But we did see the flip side of this in the feed.
• 3:36
  Speaker 0: Taha cast out shared a scenario where AI is working.
• 3:39
  Speaker 1: This is the stroke case.
• 3:41
  Speaker 1: Yes.
• 3:41
  Speaker 0: A patient arrives in the ER, right side paralyzed.
• 3:44
  Speaker 0: The initial CT scan looks normal to the human eye.
• 3:47
  Speaker 0: But minutes later, an AI model reviewing that same scan flags a subtle vessel irregularity.
• 3:53
  Speaker 0: And
• 3:53
  Speaker 1: that flag got the team to reevaluate and treat.
• 3:56
  Speaker 0: Exactly.
• 3:57
  Speaker 0: But notice the difference.
• 3:58
  Speaker 0: In the chat GPT example, the AI was the judge giving a grade.
• 4:02
  Speaker 1: Right, on its own.
• 4:03
  Speaker 0: In the stroke case, the AI was a safety net.
• 4:07
  Speaker 0: The AI didn't replace the radiologist.
• 4:10
  Speaker 0: It just caught the subtle signal that a stressed human might miss.
• 4:13
  Speaker 1: So the takeaway is AI works best as an alarm system, not the judge and jury.
• 4:19
  Speaker 0: But for that alarm system to work, the data has to be clean.
• 4:23
  Speaker 0: And Matthias Goyen raised a point that I think flies into the radar for a lot of tech teams.
• 4:27
  Speaker 0: He talked about sloppy language.
• 4:29
  Speaker 1: Right, the ethics of it.
• 4:30
  Speaker 0: Yes, it was a fascinating take.
• 4:31
  Speaker 0: We usually talk about data hygiene as a technical problem.
• 4:35
  Speaker 0: Goyen argues it's a moral one.
• 4:36
  Speaker 0: How so?
• 4:37
  Speaker 0: Well, AI models are trained on medical reports written by humans.
• 4:42
  Speaker 0: If doctors use ambiguous language or hedging or just sloppy descriptions in their notes, the AI learns to normalize that ambiguity.
• 4:49
  Speaker 1: It mirrors our bad habits back at
• 4:52
  Speaker 0: us.
• 4:52
  Speaker 0: And then it amplifies them.
• 4:54
  Speaker 0: Goyne's argument is that if we know these systems will be treating patients in the future, writing precise, clear medical notes today is an ethical obligation.
• 5:02
  Speaker 0: Wow.
• 5:03
  Speaker 1: That's a heavy realization.
• 5:04
  Speaker 1: It means good documentation is no longer just about billing or liability.
• 5:07
  Speaker 1: It's about the future intelligence of the entire system.
• 5:10
  Speaker 0: Exactly.
• 5:11
  Speaker 0: And speaking of precision, I want to touch on one specific win that came up.
• 5:14
  Speaker 0: Chris Scott or DeVito posted about page predict, which was just acquired by Tempus.
• 5:18
  Speaker 1: This felt like a very tangible solution to a very real problem.
• 5:22
  Speaker 0: It addresses the tissue economy in oncology.
• 5:25
  Speaker 0: This is something people outside of pathology rarely think about, but it's a huge bottleneck.
• 5:30
  Speaker 1: Explain that.
• 5:31
  Speaker 0: Sure.
• 5:32
  Speaker 0: When you do a biopsy, you have a tiny amount of tissue.
• 5:34
  Speaker 0: You have to slice it up to test for different biomarkers.
• 5:37
  Speaker 0: Often by the time you get to the advanced tests like next generation sequencing and GS, you've literally run out of tissue.
• 5:44
  Speaker 1: So the patient misses out on crucial genetic testing because the sample was used up on more basic tests.
• 5:49
  Speaker 1: That feels like a tragedy of logistics.
• 5:51
  Speaker 0: It is.
• 5:52
  Speaker 0: PagePredict uses standard H&E images.
• 5:55
  Speaker 0: The standard purple and pink slides you see in every lab to identify biomarkers digitally before you even cut the tissue.
• 6:02
  Speaker 1: So it's a screening tool to save the physical sample.
• 6:05
  Speaker 0: It helps clinicians decide the best sequence of tests to maximize the chance of an actionable result.
• 6:10
  Speaker 0: That is what execution looks like.
• 6:12
  Speaker 0: It's not a chatbot having a conversation.
• 6:15
  Speaker 0: It's a backend tool ensuring a cancer patient gets the right diagnosis because you didn't waste their biopsy.
• 6:20
  Speaker 1: That is a perfect transition to our second theme.
• 6:22
  Speaker 1: Let's move from the lab to the theater.
• 6:25
  Speaker 1: Digital surgery and robotics.
• 6:27
  Speaker 0: The narrative here is shifting fast.
• 6:29
  Speaker 0: For years the conversation was Look at this cool robot.
• 6:32
  Speaker 0: And
• 6:33
  Speaker 1: now.
• 6:33
  Speaker 0: Now the conversation is, how does this fit into my P&L and my workflow?
• 6:37
  Speaker 1: Right.
• 6:38
  Speaker 1: The buzzword in the posts from Rajit Kamal and James Boyle at Medtronic wasn't autonomy.
• 6:43
  Speaker 1: It was choice.
• 6:44
  Speaker 1: They were talking about the Hugo RIS system.
• 6:46
  Speaker 0: And notice the pitch.
• 6:48
  Speaker 0: They aren't saying this robot replaces everything.
• 6:51
  Speaker 0: They're pitching modularity.
• 6:53
  Speaker 1: Because hospitals are crowded and budgets are tight.
• 6:55
  Speaker 1: Exactly.
• 6:56
  Speaker 0: You can't fit a massive monolithic system in every OR.
• 7:00
  Speaker 0: The Hugo system focuses on flexibility letting surgeons use specific tools, like the Ligashire Maryland jaw or the Rubina visioning system, only when they need them for a procedure.
• 7:10
  Speaker 1: It sounds like they're admitting the one size fits all era of robotics is over.
• 7:14
  Speaker 1: It's about being part of an ecosystem, not dominating the room.
• 7:17
  Speaker 0: But does the modular approach actually deliver?
• 7:21
  Speaker 0: God will.
• 7:21
  Speaker 0: Dave shared some numbers from the expanded URL trial that caught my eye.
• 7:24
  Speaker 0: Those
• 7:25
  Speaker 1: numbers were incredibly robust.
• 7:27
  Speaker 0: They were.
• 7:30
  Speaker 0: They set a target for success at eighty-five percent.
• 7:32
  Speaker 0: They
• 7:32
  Speaker 1: hit ninety-eight point five
• 7:34
  Speaker 0: percent.
• 7:34
  Speaker 0: Ninety-eight point five percent.
• 7:36
  Speaker 0: That loops us right back to our reliability theme.
• 7:39
  Speaker 0: In the early days of robotics, there was always a fear of technical failure or latency.
• 7:44
  Speaker 0: Hitting nearly ninety-nine percent efficacy in a clinical trial builds the one thing you can't buy.
• 7:50
  Speaker 0: Surgeon trust.
• 7:51
  Speaker 1: Because if a surgeon doesn't trust the machine, it just becomes an expensive coat rack in the corner of the OR.
• 7:56
  Speaker 0: Exactly.
• 7:57
  Speaker 0: But even with that ninety-nine percent success rate, Gavin Setzen provided a bit of a reality check in the feed.
• 8:03
  Speaker 1: The adoption lag.
• 8:04
  Speaker 1: Right.
• 8:04
  Speaker 0: Setzen warned that we shouldn't expect robots in every local hospital next week.
• 8:08
  Speaker 0: Broad adoption is measured in years, not months.
• 8:11
  Speaker 0: Capital cycles, training.
• 8:13
  Speaker 0: It all takes time.
• 8:13
  Speaker 1: He emphasized we're in the age of augmentation, not autonomy.
• 8:17
  Speaker 0: And that connects to the Surgeon of the Future concept that Christian Masi mentioned.
• 8:20
  Speaker 0: Yeah.
• 8:20
  Speaker 0: The robot isn't doing the surgery for you while you drink a coffee.
• 8:23
  Speaker 0: Why from
• 8:23
  Speaker 1: it?
• 8:24
  Speaker 0: The surgeon is still accountable at the head of the table.
• 8:26
  Speaker 0: The robot gives you supervision and super-steady hands, but it doesn't make the decisions.
• 8:32
  Speaker 0: We are a long, long way from an autonomous droid performing a bypass.
• 8:38
  Speaker 1: Agreed.
• 8:39
  Speaker 1: Let's move out of the OR.
• 8:41
  Speaker 1: and look at the messy world of the consumer.
• 8:44
  Speaker 1: Theme three, wearables, remote monitoring, and prevention.
• 8:48
  Speaker 0: This section had some of the most human insights of the week.
• 8:53
  Speaker 0: There's a growing backlash against the idea that more data equals better health.
• 8:57
  Speaker 1: Bianca Carbone had the core of the week on this.
• 9:00
  Speaker 1: She said, AI can give you all the data in the world, but, and I love this, AI can't pick the dumbbells up for you.
• 9:06
  Speaker 0: It's a sharp critique of the whole quantified self movement.
• 9:09
  Speaker 0: We confuse information with support.
• 9:11
  Speaker 0: Right.
• 9:11
  Speaker 0: Our bonus point is that people don't fail to get healthy because they lack data.
• 9:14
  Speaker 0: They know they should sleep more.
• 9:16
  Speaker 0: They fail because they lack accountability, community, and, you know, the actual mechanisms for behavior change.
• 9:21
  Speaker 1: She basically said, don't spend thousands on health tech if you haven't mastered the basics.
• 9:25
  Speaker 1: It reminds me of what Mohita Hujo was observing out in Dubai.
• 9:28
  Speaker 0: The biohacking trend.
• 9:30
  Speaker 0: He described clinics flooded with people asking for peptides, cryotherapy, all these advanced supplements.
• 9:36
  Speaker 1: But these are the same people who are sleeping four hours a night and are totally stressed out.
• 9:40
  Speaker 0: Exactly.
• 9:41
  Speaker 0: They are trying to tech their way out of a bad lifestyle.
• 9:45
  Speaker 0: Ahuja's insight is simple but devastating.
• 9:49
  Speaker 0: Performance doesn't compound.
• 9:50
  Speaker 0: Foundations do.
• 9:52
  Speaker 0: You can't use tech to bypass the need for sleep and nutrition.
• 9:55
  Speaker 1: It's like putting rocket fuel in a car with a flat tire.
• 9:58
  Speaker 1: Precisely.
• 9:59
  Speaker 0: That said, for people who are managing a chronic condition, there is some hardware on the horizon that looks promising.
• 10:05
  Speaker 0: Wonsang called it the holy grail.
• 10:07
  Speaker 1: non-invasive glucose monitoring.
• 10:10
  Speaker 1: This has been the next big thing.
• 10:12
  Speaker 1: for what, a decade?
• 10:13
  Speaker 1: Are we actually getting closer?
• 10:15
  Speaker 0: Sang notes the race is getting very real for twenty twenty six.
• 10:18
  Speaker 0: The impact would be massive.
• 10:20
  Speaker 0: But she also points out the physics problem.
• 10:22
  Speaker 0: It's incredibly difficult to get an accurate reading through the skin using light or radio waves.
• 10:26
  Speaker 1: Too many variables.
• 10:27
  Speaker 1: Hydration, skin tone,
• 10:29
  Speaker 0: skin temperature.
• 10:30
  Speaker 0: All of it.
• 10:31
  Speaker 0: It goes back to the young chaw point about reliability.
• 10:34
  Speaker 0: If your watch says your blood sugar is low, but it's actually high because you're dehydrated, that's dangerous.
• 10:40
  Speaker 1: So the first company to crack that reliability barrier wins.
• 10:44
  Speaker 0: Wins the whole market.
• 10:45
  Speaker 0: But there is another barrier besides physics.
• 10:48
  Speaker 0: Cost.
• 10:49
  Speaker 0: Simon Neve highlighted this with a post about Leap Health.
• 10:52
  Speaker 1: Yeah, I saw that stat.
• 10:53
  Speaker 1: Fifty-four percent of people cite cost as the number one barrier to sleep tracking.
• 10:59
  Speaker 1: The big, brand smart rings are expensive.
• 11:01
  Speaker 0: Prohibitive for the average person.
• 11:03
  Speaker 0: So Leap Health launched a smart ring specifically focused on affordability.
• 11:07
  Speaker 0: It's a feature-focused device, doesn't do everything, but it tracks sleep at a price point normal people can actually afford.
• 11:13
  Speaker 1: It's democratizing the data, because if only the wealthy can afford to track their sleep, we aren't solving public health problems.
• 11:19
  Speaker 0: We're just optimizing the elites.
• 11:21
  Speaker 0: And speaking of democratization, we have to talk about the story from Atul Gupta.
• 11:25
  Speaker 0: This was my favorite example of execution this week because of where it happened.
• 11:29
  Speaker 1: The Wood Green Community Diagnostic Center.
• 11:31
  Speaker 0: Yes.
• 11:32
  Speaker 0: Imagine walking into a shopping mall in north London.
• 11:36
  Speaker 0: Between the stores and the food court, there is a Phillips blue seal MRI scanner.
• 11:41
  Speaker 1: a helium-free MRI in a mall.
• 11:43
  Speaker 1: That's what lets them do it, right?
• 11:44
  Speaker 1: It eliminates the need for the massive venting
• 11:47
  Speaker 0: pipes.
• 11:47
  Speaker 0: Exactly.
• 11:48
  Speaker 0: But look at the demographic data Gupta shared.
• 11:51
  Speaker 0: Seventy-five percent of the patients using that scanner are from disadvantaged households.
• 11:55
  Speaker 0: This is bringing the highest tech AI smart speed imaging directly to the people who usually face the longest wait times.
• 12:03
  Speaker 1: That is massive.
• 12:04
  Speaker 1: It challenges the assumption that high tech always means exclusive.
• 12:07
  Speaker 0: It proves that execution isn't just about the machine, it's about the logistics.
• 12:12
  Speaker 0: Putting the machine where the people are, that is true innovation.
• 12:15
  Speaker 1: Okay, so we've covered the AI reliability gap, the robotic workflow, the consumer reality check, but none of this works if the companies are dysfunctional.
• 12:23
  Speaker 1: Let's talk about our final theme, strategy, leadership, and these energy vampires.
• 12:28
  Speaker 0: This is a crucial warning for any health tech leader listening.
• 12:31
  Speaker 0: Meenal Shah introduced this concept of pilot churn as an energy vampire.
• 12:36
  Speaker 1: It's an evocative term.
• 12:37
  Speaker 1: What does she mean by it?
• 12:38
  Speaker 0: We've all seen it.
• 12:39
  Speaker 0: A hospital runs a pilot program for a new tool.
• 12:42
  Speaker 0: It runs for six months, shows some decent results, but then...
• 12:46
  Speaker 1: Nothing.
• 12:47
  Speaker 1: The budget isn't approved or a priority shift.
• 12:49
  Speaker 0: Right.
• 12:49
  Speaker 0: The pilot just dies.
• 12:50
  Speaker 0: Right.
• 12:51
  Speaker 0: Then, three months later, management introduces another pilot for a different tool.
• 12:56
  Speaker 0: And the clinicians just roll their eyes.
• 12:58
  Speaker 0: They're
• 12:58
  Speaker 1: burned out.
• 12:59
  Speaker 0: Exactly.
• 13:00
  Speaker 0: Shaw argues the cost isn't just the money, it's the erosion of clinician trust.
• 13:05
  Speaker 0: If you tell a doctor, here's a new tool that will change your life and then it disappears.
• 13:10
  Speaker 0: They are not going to believe you next time.
• 13:12
  Speaker 0: You've exhausted their capacity for change.
• 13:14
  Speaker 1: So when the real innovation finally arrives, the staff is too cynical to adopt it.
• 13:19
  Speaker 0: That is the vampire.
• 13:20
  Speaker 0: It sucks the energy out of the organization.
• 13:22
  Speaker 0: And to prevent that, you need better leadership.
• 13:25
  Speaker 0: Ian Robertson had a very strong take on who needs to be in charge.
• 13:28
  Speaker 1: He talked about tandem success coming from having clinicians in leadership, not just as advisors.
• 13:34
  Speaker 0: He used this brilliant analogy, he said, put a referral letter in front of an engineer.
• 13:38
  Speaker 0: and they see data transfer, a file moving from point A to point B.
• 13:42
  Speaker 1: But put that same letter in front of a doctor.
• 13:44
  Speaker 0: And they see their professional reputation.
• 13:47
  Speaker 0: That letter represents their judgment, their relationship with the specialist, their care for the patient.
• 13:52
  Speaker 0: That
• 13:52
  Speaker 1: nuance is everything.
• 13:53
  Speaker 1: If you build a tool that just transfers data... but ignores the professional tone of a referral, doctors won't use it.
• 14:00
  Speaker 0: Precisely.
• 14:00
  Speaker 0: You need that clinical DNA in the C-suite to understand those unspoken rules.
• 14:05
  Speaker 0: Otherwise, you're just building efficient products that no one actually wants.
• 14:08
  Speaker 1: But even with great leadership, you're operating in a minefield of regulation.
• 14:13
  Speaker 1: And Rudolph Ragnar sounded a massive alarm bell this week regarding SAMD software as a medical device.
• 14:19
  Speaker 0: Ragnar's analysis was shocking.
• 14:21
  Speaker 0: He claims SAMD regulation has essentially collapsed due to a total failure of enforcement.
• 14:26
  Speaker 0: Collapsed
• 14:27
  Speaker 1: is a strong word.
• 14:28
  Speaker 1: What's the evidence?
• 14:28
  Speaker 0: He analyzed ninety six thousand health apps on the app stores.
• 14:32
  Speaker 0: He found that ninety five percent of them, ninety five percent, had no verifiable CE mark or FDA authorization.
• 14:39
  Speaker 1: Wait, ninety five percent of health apps are unregulated, so the app store is basically the Wild West.
• 14:44
  Speaker 0: It's worse.
• 14:45
  Speaker 0: It's an unregulated pharmacy.
• 14:47
  Speaker 0: You have apps making diagnostic claims, recommending treatment, scoring risk, all without any regulatory oversight.
• 14:53
  Speaker 0: Wagner argues the app stores have effectively become distributors of illegal medical devices, and the regulators are silent.
• 15:00
  Speaker 1: That circles right back to the start of our conversation about trust.
• 15:03
  Speaker 1: If ninety-five percent of these tools are unverified, how can any patient possibly know what is safe?
• 15:08
  Speaker 0: They can't.
• 15:09
  Speaker 0: And that is a ticking time bomb for the whole industry.
• 15:12
  Speaker 0: However, we shouldn't take this as a sign to just give up on regulation.
• 15:15
  Speaker 0: No.
• 15:16
  Speaker 0: Sigurd Burs van Roigen offered a necessary counter perspective on the EU AI Act.
• 15:21
  Speaker 1: She was defending the new regulations.
• 15:23
  Speaker 1: In a
• 15:23
  Speaker 0: way, she noted that a lot of companies use the EU AI Act as an excuse to stop innovating.
• 15:28
  Speaker 0: They say, oh, the compliance is too hard.
• 15:30
  Speaker 0: We can't build here.
• 15:31
  Speaker 0: She argues that's lazy thinking.
• 15:34
  Speaker 0: Regulation should be viewed as a necessary baseline for safety.
• 15:37
  Speaker 1: It's not a roadblock.
• 15:38
  Speaker 1: It's the guardrails.
• 15:39
  Speaker 0: Exactly.
• 15:40
  Speaker 0: If we want AI to be standard care, it has to be regulated.
• 15:43
  Speaker 0: Innovation and safety aren't opposites.
• 15:45
  Speaker 0: They are partners.
• 15:46
  Speaker 0: If you can't build your product within safety regulations, you probably shouldn't be in health care.
• 15:51
  Speaker 1: So let's zoom out.
• 15:53
  Speaker 1: We've gone from an AI giving a reporter a fake heart condition to robots hitting ninety eight percent reliability to an MRI in a shopping mall in a regulatory crisis.
• 16:03
  Speaker 1: What is the through line here?
• 16:05
  Speaker 0: If I connect these docs, the message for CW four and five is clear.
• 16:09
  Speaker 0: The demo era is over.
• 16:11
  Speaker 0: It is no longer enough to show something cool.
• 16:14
  Speaker 0: You have to show something that is reliable that respects the clinicians workflow and that can survive scrutiny.
• 16:20
  Speaker 1: It feels like the industry is maturing.
• 16:21
  Speaker 1: We're moving from.
• 16:22
  Speaker 1: can we build it to?
• 16:24
  Speaker 1: should we build it and does it actually
• 16:25
  Speaker 0: work?
• 16:26
  Speaker 0: And is it sustainable?
• 16:27
  Speaker 0: That is a much harder question to answer.
• 16:29
  Speaker 0: It requires patience, clinical leadership, and a willingness to do the boring work of execution rather than just the fun work of invention.
• 16:36
  Speaker 1: And that leaves us with the provocative question for you, the listener, to chew on this week.
• 16:41
  Speaker 0: Indeed.
• 16:42
  Speaker 0: Look at your own organization.
• 16:44
  Speaker 0: Are you building for the demo, for the flash, the VC deck, the hype?
• 16:48
  Speaker 0: Or are you building for the product, the reliability, the workflow, the trust?
• 16:53
  Speaker 0: Are you feeding the energy vampire with endless pilots?
• 16:56
  Speaker 0: Or are you building the foundations for long-term execution?
• 16:59
  Speaker 1: That is the question.
• 17:01
  Speaker 1: The execution frontier is where the winners will be determined this year.
• 17:04
  Speaker 1: Absolutely.
• 17:05
  Speaker 1: If you enjoyed this episode, new episodes drop every two weeks.
• 17:07
  Speaker 1: Also, check out our other editions on ICT and tech insights, defense tech, cloud, digital products and services, artificial intelligence and sustainability and green ICT.
• 17:16
  Speaker 0: Thank you for listening.
• 17:17
  Speaker 1: Don't forget to subscribe for the next deep dive into the execution frontier.
• 17:21
  Speaker 1: See you then!