AI Health Apps: How to Win?
While the global healthcare AI market grew to an estimated $30-40 billion in 2025 (an estimated rise of between 35-45% from 2024) with the sector now adopting AI at twice the rate of the broader economy, a stark reality persists: the gap between hype and successful execution has never been wider.
The numbers paint an exciting picture. Over 950 FDA-approved AI tools are now in use, and the market is projected to reach $419 billion by 2033. Yet behind these headline-grabbing figures lies a cold truth: many AI health apps lack the scientific evidence, regulatory clarity, and business fundamentals needed for long-term success.
What Winners Have in Common
Winners solve important problems
Many AI apps are built because “the model works,” not because the model matters, a situation that has parallels with IBM in the early 90s which led to its near bankruptcy. Teams often optimise performance on their available datasets rather than focusing on addressing a genuine unmet clinical or operational need.
Common symptoms of this pitfall include:
Solutions that don’t fit into any real clinical workflow
Products aimed at problems with no payer, no budget, or no incentive to solve
Mobile apps designed around user curiosity, not sustained behaviour change
Start with the problem, not the model. Build around well-defined needs, validated with clinicians, payers, and patients. Focus on outcomes that matter. In short, solve real, “expensive” problems.
Winners build regulatory compliance
Many consumer health apps cleverly market themselves as "wellness" tools to avoid the scrutiny of the regulatory authorities, but this strategy creates long-term vulnerability. Health authorities are increasingly scrutinizing AI-enabled mental health devices, and their regulatory frameworks are rapidly evolving. Apps designed to avoid regulation today may find themselves non-compliant tomorrow, or worse, face liability issues when something goes wrong.
Build-in regulatory compliance from day one, even if not strictly required, anticipating stricter future oversight rather than evading it. Invest in regulatory relationships, maintain critical certifications (HIPAA, SOC 2, ISO 27001), and design systems for continuous monitoring and improvement. Far from being a purely “defensive” strategy, compliance directly impacts the bottom-line, potential investment and can accelerate access to market.
Winners recognise the data challenge
AI is only as intelligent as the data it is trained on. Many health apps fail spectacularly when confronted with real-world data as opposed to their training sets. Furthermore, the most valuable health data is increasingly locked behind commercial, regulatory, , and ethical barriers. The “open data” era is over.
Prioritize data quality, diversity of training populations, and continuous real-world validation over feature proliferation. Invest in continuous data quality improvement and treat responsible data governance as a core competence.
Winners focus on the Business Model
Too many AI health apps launch without clearly understanding how to demonstrate measurable value, who pays, and why they'll pay repeatedly. The ambient scribe category (healthcare AI's breakout success generating $600 million in the US alone, in 2025) now faces customer stickiness problems, with many health systems willing to switch vendors. While consumer apps dominate headlines, the real value is captured in B2B relationships with healthcare providers accounting for over 30% of AI healthcare revenue.
Focus relentlessly on demonstrable ROI. Whether through reducing documentation time, achieving specific clinical outcomes, or enabling operational cost savings, successful apps prove value in hard metrics, not aspirational benefits. Target Institutional Buyers, not just Consumers and build for the long-term.
The Investment Thesis
The AI health app market will continue growing rapidly but success will concentrate among a relatively small cohort of Apps. For investors evaluating AI health apps, the decision framework is straightforward:
Does the app solve a workflow problem, a clinical need and/or costs institutions real money?
Is the regulatory strategy proactive or evasive?
Is there a commitment to data improvement and governance?
Is the business model based on recurring, defensible value?
Dr Ivan Fisher, Peter Leister