Why Evidence Must Guide the Rise of HealthTech Innovation
Healthtech is one of the fastest-growing segments in healthcare, resulting from the rapid adoption of artificial intelligence (AI), machine learning (ML), and digital tools to help improve administrative workflows, telehealth services, and maintain patient health outside doctor offices. Patients everywhere are increasingly turning to AI chatbots to inquire about symptoms, relying on wearables to monitor their cardiometabolic health, and providers have started to implement technologies to automate operations and smoothen administrative procedures. While all of these have shown to streamline healthcare delivery and ease the patient experience, it is important to consider the need for research-backed validation prior to authorizing healthtech products and services, since, unlike most industries, risk in healthcare is substantial. In other tech industries, a product bug or malfunction would cause a financial loss or slight inconvenience, while in healthcare, tools made without evidence can lead to direct patient harm- potentially leading to life-risking outcomes and major legal risks.
Most people wouldn’t trust drugs without proper trials, so why implement algorithms into healthcare without the proof?
When developing digital health tools, founders tend to calculate metrics including precision and accuracy based on curated datasets. A study analyzing factors of success for growth stage digital health companies found that the most common factors included financials and market demand, while regulation was cited to be in the “external” success factors [1]. This highlights a tension in healthtech- prioritizing status in the marketplace and financial gain over evidence-backed validation and long-term applicability. Concerns relating to data privacy, regulation, and ethics must also be addressed to ensure safe implementation and reach [2]. Testing a new product with premade datasets is not enough to ensure the product will be sustainable in real-world scenarios, despite how closely curated data can mimic them. It may be even more difficult to mimic conditions in rural or underserved areas, an important consideration for achieving widespread reach.
When developing new technologies, it is crucial to include unbiased, underrepresented populations and data that may be typically missed in training phases to properly represent the comprehensive audience. For example, many medical wearables lack diversity in their training and are heavily based on “lighter-skinned” populations [3]. This not only significantly narrows the credibility of the product, but it also discriminates against individuals who may also benefit from it. Biased and limited algorithms create gaps in the performance of health technologies, prohibiting them from reaching larger populations.
Research-driven AI is evaluated like health interventions, not just digital products. There are stages involved in the assessment of a product, which can include pilot testing and outcomes tracking after deployment. Development phases of a new technology can include formation, validation, and scaling phases [4], all often inevitably geared towards market fit. These stages illustrate how the intricate process of development is essential for creating an evidence-based, sustainable, and widely-adopted product.
In healthtech, evidence speeds up the likelihood for hospitals and health systems to adapt to new technologies, giving them reasons to trust investing their time and resources, and exposing their patients to the possible risks associated with digital health tools. While health technologies provide numerous benefits to systems, they can also contribute to streamlining policy-making and implementation practices. HTAs (Health Technology Assessments) use a multidisciplinary approach to synthesize available evidence about the consequences of an intervention to inform policy and practice [5]. Although not fully established yet, health tech can provide an effective strategy to develop strong and sustainable public health interventions by clearly forecasting probable repercussions as well as expected benefits.
Health technology has significant potential to advance the reach and effectiveness of health interventions implemented by NGOs and aid organizations by improving coordination, accessibility, and scalability of services in resource-constrained settings. This sector presents an opportunity for builders and investors to seek an impact [6], especially given all the AI-powered technologies that already make an impact in the lives of many, including AI-powered chatbots, which engage with patients, offer medical advice, and provide online training sessions for healthcare workers in rural areas [7]. As digital infrastructure continues to grow, applying this to implementation science proposes strategic methods to close gaps in public health, strengthen health systems, and maintain sustainable improvements in population health.
References
1. Pfitzer, E., Bitomsky, L., Nißen, M., Kausch, C., & Kowatsch, T. (2024). Success factors of growth-stage digital health companies: a systematic literature review (Preprint). Journal of Medical Internet Research, 26, e60473–e60473. https://doi.org/10.2196/60473
2. Ahmed, I. (2025). Innovation in Healthtech: The Role of Artificial Intelligence in Shaping Future Markets. Researchcorridor.org. https://researchcorridor.org/index.php/jbai/article/view/450/430
3. Lee, S., & Akamatsu, K. (n.d.). Foundation Models for Physiological Signals: Opportunities and Challenges. https://openreview.net/pdf?id=u3nat9mOIo
4. Higgins, D., & Madai, V. I. (2020). From Bit to Bedside: A Practical Framework for Artificial Intelligence Product Development in Healthcare. Advanced Intelligent Systems, 2(10), 2000052. https://doi.org/10.1002/aisy.202000052
5. Cyr, P. R., Jain, V., Chalkidou, K., Ottersen, T., & Gopinathan, U. (2021). Evaluations of public health interventions produced by health technology assessment agencies: A mapping review and analysis by type and evidence content. Health Policy, 125(8), 1054–1064. https://doi.org/10.1016/j.healthpol.2021.05.009
6. Morgan, J. P. (2025). 2025 Healthtech Trends. J.P. Morgan. https://www.jpmorgan.com/insights/markets-and-economy/outlook/2025-healthtech-trends
7. Varisha Zuhair, Babar, A., Ali, R., Malik Olatunde Oduoye, Noor, Z., Chris, K., Inibehe Ime Okon, & Latif Ur Rehman. (2024). Exploring the Impact of Artificial Intelligence on Global Health and Enhancing Healthcare in Developing Nations. Journal of Primary Care & Community Health, 15(1). https://doi.org/10.1177/21501319241245847
More about the author!
Anushka Purkayastha holds an undergraduate degree in Public Health from Rutgers University–New Brunswick and is an incoming MPH student at New York University. Her work focuses on health services research and implementation science at the intersection of legal policy, corporate influence, and healthcare delivery, with a particular interest in cross-sector partnerships in health technologies and integration models that address barriers to care.
