Meet the AI Product Manager Transforming Healthcare

In the age of artificial intelligence, healthcare stands on the edge of a seismic transformation. AI-powered products are no longer theoretical—they’re already impacting clinical workflows, improving detection rates, and challenging traditional assumptions about the roles of physicians and technology. Nowhere is this more apparent than in the work of Keshav Swarup, Product Manager at Iterative Health, who joined the American Journal of Healthcare Strategy podcast to discuss how AI is quietly—and profoundly—raising the bar for patient outcomes.

Why does this conversation matter today? Because U.S. healthcare costs exceed $4 trillion annually and outcomes have stagnated despite escalating spending. Yet, a new breed of leaders—AI product managers with both technical savvy and human-centered vision—are finding ways to drive real value at the frontlines of care. This episode offers a rare, behind-the-scenes perspective on what it takes to design, launch, and scale healthcare AI products in a world still skeptical about digital transformation. If you’re a healthcare executive, clinician, or student plotting your next career move, this conversation delivers actionable insight into the future of the industry.

What Does an AI Product Manager Do in Healthcare, and Why Should You Care?

Short answer: An AI product manager in healthcare identifies critical problems, designs solutions using technology, and ensures both clinical value and business viability. This unique function is crucial for anyone interested in how innovations like computer vision move from concept to clinical practice.

“A product manager’s role is primarily to help you develop and deliver business impact…solving the most important customer problems that deliver the most value and impact to them,” says Swarup.

But unlike their tech counterparts, healthcare product managers juggle additional variables:

• Patient safety and privacy concerns (HIPAA, FDA regulations)

• Complex reimbursement dynamics

• Provider adoption and trust

• Data quality and clinical validation

Swarup’s day-to-day reflects this complexity: “You want to make sure that it’s valuable to the user…so a lot of my time is spent being the voice of the customer internally, understanding their needs, their pain points, what does their incentives look like.”

In short, the AI product manager acts as a bridge—translating clinical realities, regulatory constraints, and cutting-edge technology into products that can survive the gauntlet of real-world implementation.

From Material Science to AI in Gastroenterology: One Nonlinear Career Path

Short answer: You do not need an MBA or a computer science PhD to thrive as an AI product manager in healthcare. Diverse backgrounds—when paired with curiosity and hands-on learning—can lead to breakthrough impact.

Keshav Swarup’s journey began with a bachelor’s in material science from Georgia Tech and an early career in 3D printing for oral health. “My background was in Material Science and Engineering…I got into 3D printing right out of school…for dental products like Invisalign.”

How did he pivot to AI? The trigger was personal: Swarup’s mother spent years undiagnosed with Crohn’s disease. When he saw a Cambridge startup aiming to use AI for inflammatory bowel disease (IBD) trial recruitment, the mission resonated: “It just resonated so much with a problem that affected my mother and the rest of my family.”

Key lessons for aspiring product managers or healthcare leaders:

• Industry switching is possible: Core skills—customer empathy, systems thinking, and project management—translate across verticals.

• On-the-job learning is critical: “A lot of it came from on the job and watching a lot of really successful executives, leaders, and folks who are a bit more on the commercial side.”

• Mission matters: Personal passion for the problem sustains you through the complexity and ambiguity of healthcare innovation.

Breaking into AI: Is Deep Technical Expertise Essential?

Short answer: No. You don’t need to be an AI engineer to succeed as an AI product manager—but you must be willing to learn enough to make informed decisions and collaborate credibly with technical teams.

Swarup admits the learning curve was steep: “It was not an easy transition…I had to learn a lot about the clinical space of IBD and then on the AI front…first understand just machine learning and then go deeper into deep learning, which is the specific technology for computer vision algorithms.”

How did he get up to speed?

• “Talking with friends in Silicon Valley working on self-driving cars and drone delivery.”

• Consuming free online resources: “YouTube, newsletters, courses—there’s some great content that is really simplified down to any layman.”

His advice:

1. Master the basics—then go deep only if it fits your “superpowers.”

2. Leverage your network—surround yourself with experts and keep asking questions.

3. Balance theory with practice—stay close to real products, users, and implementation.

Where Is AI in Healthcare Going—And What Should Leaders Watch?

Short answer: AI in healthcare is at an inflection point. While generative AI grabs headlines, computer vision and workflow automation are already transforming care delivery. Expect massive growth, but with growing pains around regulation, reimbursement, and adoption.

Swarup describes himself as “cautiously optimistic” about AI’s impact, explaining, “We are truly in a renaissance of artificial intelligence…computer vision, natural language processing, and others aren’t getting as much attention because maybe they’re less consumer facing.”

For executives, several trends are clear:

• AI’s value proposition is access and cost: “If you can make a nurse practitioner or a doctor more productive, the cost can start to decrease over time.”

• Ambient AI is everywhere: “Over 40 AI companies are working on ambient scribe technology,” promising to free up clinician attention.

• Adoption will be uneven: “There is a provider-facing value…you can then detect cancer earlier, more consistently, standardize these disparities that exist in outcomes.” But payers and regulators need to catch up.

Swarup sees a future where AI augments—not replaces—clinicians: “There is still going to be this symbiotic relationship…certain tasks that we should let the technology take on and augment us, whereas the physician can then focus on the other pieces.”

Inside Iterative Health: How AI Is Reshaping Colonoscopy and Clinical Trials

Short answer: Iterative Health deploys AI-powered computer vision to help gastroenterologists detect more polyps during colonoscopy—raising both the consistency and quality of cancer screening. The company also uses AI to optimize patient recruitment for IBD clinical trials.

Swarup explains: “Scout is an AI-assisted colonoscopy algorithm…meant for colonoscopists in real time to help them detect polyps and so we use computer vision and AI algorithms to help them detect more polyps, increase their detection rates, and hopefully have a higher quality of colonoscopies.”

Key benefits of Scout:

• Standardizes detection rates across providers and time of day

• Reduces disparities in outcomes (less experienced or fatigued physicians can perform at the level of the best)

• Provides real-time feedback, supporting earlier and more reliable cancer detection

On the clinical trials front:
“We use AI and computer vision compounded with services to help Pharma companies recruit patients by identifying the best candidates for drug trials…you can do that now in the matter of minutes instead of having to manually screen through all the data.”

Why it matters:

• AI’s value is not just automation, but raising the standard of care and opening access to new treatments for underserved patients.

• For health systems, adopting such tools can improve clinical outcomes, enhance physician satisfaction, and potentially reduce long-term costs.

Will AI Replace Clinicians? Understanding the Augmentation vs. Automation Debate

Short answer: AI in healthcare is not about replacing doctors, but augmenting them. While some back-office roles may decline, demand for clinical expertise, judgment, and care coordination will only increase.

Swarup addresses common fears head-on:
“Folks were putting out all sorts of statement pieces about radiologists not existing…look where we are today and there’s still, in fact, actually a shortage of radiologist because a lot of folks…were afraid to get into radiology.”

Expect these changes:

• Some tasks will be automated: e.g., revenue cycle management, scheduling, basic documentation.

• New roles will emerge: AI workflow operators, clinical data strategists, human-in-the-loop supervisors.

• Core physician tasks remain: Clinical reasoning, empathy, cross-disciplinary care, and “connecting the dots.”

“There will be some jobs…that may cut down in the number of roles; however, there will be other roles that will open up just with any other new technology.”

Is Now the Right Time to Pivot Your Career into AI in Healthcare?

Short answer: Yes—if you are willing to learn, adapt, and align your skills with where the industry is headed. AI is a growth area, but entry remains competitive and nuanced.

Swarup offers direct advice:
“It is absolutely a great investment to start with just something and start learning as fast as you are interested…those investments that I’ve made, they’re not in the order of thousands of dollars—some of them are free.”

To enter the field:

• Identify your superpowers: Are you more technical, clinical, operational, or strategic?

• Build foundational tech literacy: Basic online courses (Andrew Ng’s Coursera, Harvard/Amazon free AI modules) are enough to start.

• Network: Join communities like Health Tech Nerds, Fierce Healthcare, or Twitter/LinkedIn groups.

• Look for non-traditional roles: Clinical advisory, product operations, regulatory strategy—all intersect with AI.

Swarup is candid about competition:
“It’s highly competitive…there was this massive influx of folks who are available for roles…but the number of startups is just growing…the supply is getting there, but their bar for hiring is also not lowering.”

What Are the Real Barriers to Widespread AI Adoption in Healthcare?

Short answer: Regulatory approval, reimbursement models, and standards of care remain major bottlenecks. Payers and professional societies must buy in before AI products become ubiquitous.

“We would love for Scout to be in every endoscopic suite attached to every tower…but there is an element of the market that is not quite ready yet…it’s a fee-for-service system, we need help from payers to reimburse AI technology.”

Barriers include:

• Reimbursement: AI tools must prove cost-effectiveness and clinical value to earn CPT codes or CMS payment.

• Evidence base: “It takes millions of dollars, thousands of patients, and many years of interval cancer studies.” Early “temporary codes” may help, but long-term adoption demands robust randomized controlled trials.

• Professional guidelines: Societies must raise the bar for detection rates—“right now there’s only a 25% detection rate that is required…we need physician societies to say with AI, we can raise the bar to 30 or 35%.”

“We need some of the physician societies to raise the bar…with AI, that is where you should be.”

Recommended Resources: Where Should You Start Learning About AI in Healthcare?

Short answer: Start with a mix of free, trusted, and community-driven resources tailored to your preferred learning style.

Swarup’s favorites:

• Podcasts: a16z Podcast, especially their “Raising Health” series

• Newsletters: Health Tech Nerds (“$150 for a year and you get access to events, socials, and direct access to founders and VCs”), Fierce Healthcare, Rock Health

• Social Media: Twitter/X for daily news pulse checks, especially on technical advances

• Online Courses: Andrew Ng’s Coursera series, free YouTube tutorials

• Community: “Health Tech Nerds is a great community…you can meet physicians, founders, VCs.”

Pro Tip: Avoid content overload—focus on 1-2 channels, go deep, and use community events to build your network.

Takeaway: AI in Healthcare Demands Bridge-Builders, Not Just Coders

If there’s one insight for healthcare leaders, it’s this: The future belongs to those who can bridge clinical insight, technical curiosity, and product thinking. You don’t need to be a machine learning engineer to transform care—what matters most is the ability to empathize with users, synthesize new information, and drive impact across disciplines.

As Swarup reflects: “Having that elementary understanding then allows you to say, okay, from here now I understand the basics—where do I want to go?”

For executives, clinicians, and advanced students, the message is clear: The AI transformation in healthcare is underway. The smartest investment you can make is not just in technical skills, but in cultivating the mindset and relationships needed to turn promise into practice. Start learning, keep connecting, and be ready for the future—because it’s arriving faster than you think.