Velmio is a startup that I co-founded to build AI-powered tools for women’s health issues that have historically been underrepresented in medical research and product development. Women’s health encompasses female-specific conditions, as well as conditions that affect women differently or disproportionately. Innovations in artificial intelligence for personalised health developed in my research can benefit these patient populations. At Velmio, we built digital health apps integrating data from wearables and other sources with AI technologies to provide women with timely, accurate and tailored analyses of their health.

From digital health applications for women's health to AI research for precision medicine

More than reproductive health

"Women's health" is typically associated with reproductive health conditions, such as menstrual disorders and pregnancy complications, as well as women's oncology (e.g., breast cancer, ovarian cancer). However, women's health encompasses a broader range of health challenges, including conditions that more commonly or more severely affect women (e.g., autoimmune diseases, migraines, osteoporosis and cardiovascular disease). Moreover, gender bias in care delivery can lead to underdiagnosis, undertreatment, or misdiagnosis of women's symptoms, especially in the areas of pain management and mental health.

The need for better women's health solutions

Women's health issues have been historically underrepresented in medical research and product development. For example, early research in cardiovascular disease largely focused on male subjects. Today, women are 50% more likely to be misdiagnosed following a heart attack and are more likely than men to die from a heart attack [1]. Women have been historically underrepresented in clinical trials, and conditions that more commonly affect women receive less funding for medical research - even for conditions with a significant burden of disease [2]. Consequently, women are twice as likely as men to experience adverse events from drugs [3].

Building a digital health platform for women's health

At Velmio our mission was to build digital health applications that would improve the patient experience and patient health outcomes for underrepresented groups. The first product that we developed was the world's first digital therapeutic mobile app for reducing the risk of health complications during pregnancy.

The "data gap" in women's health refers to the lack of adequate and accurate data on various aspects of women's health, such as their specific needs, risks and outcomes [1,4]. We saw this problem as an opportunity to build a digital platform for collecting and analyzing high quality datasets for women's health, to help patients and their healthcare providers achieve personalised and evidence-based care.

To this end, we built a mobile health (mHealth) application to securely connect patient data from hundreds of sources (wearables, dietary logs, etc.), creating a "digital twin" of a patient's health and lifestyle. We then implemented machine learning algorithms to facilitate prevention, early detection and monitoring of health issues​.

Technological achievements in the Velmio pregnancy health app included:

• Connecting high quality data sources that had not previously been analysed together in a mobile application in the pregnancy health space, including digital biomarkers derived from consumer wearable devices to track the physiological changes that occur during pregnancy and environmental data to measure the impact of air pollution on pregnancy health

• Developing advanced natural language processing (NLP) technology to replace generic health information with insightful and personalised advice. For example, during the COVID-19 pandemic we analysed social media content to identify sources of stress at each stage of pregnancy, so that our app could better support the mental health of pregnant women

• Providing digital health tools to help patients manage pregnancy complications, such as gestational diabetes, pre-eclampsia, and severe morning sickness. Velmio was the first pregnancy app to offer a dedicated set of tools for patients with gestational diabetes, the most common pregnancy complication

• Developing AI/ML tools to improve the user experience for health tracking. This included our own on-device image recognition and semantic search models for nutrition tracking, as well as a machine learning tool that analysed ingredient composition and medical guidelines to indicate foods that are safe to eat during pregnancy

The Velmio pregnancy health app was utilised by over 30,000 people across 90 countries. We later extended our product offering to a second app for pregnancy care providers (including doulas, midwives), to create a seamless interaction between healthcare providers and our patient-centric solution.

Our work in the digital health space garnered global attention, being featured by the likes of Sifted/Financial Times, CNN, and EU-Startups. We also repurposed our technology to build one of the first COVID-19 health apps in the world, which was a winning entry of Estonia's national coronavirus hackathon (Forbes article) and featured by the World Health Organisation (WHO).

A more general problem

The women's health challenges I was working on at Velmio relate to a broader issue in health and medicine - solutions designed for a general patient population often fail to meet the needs of specific groups, like women. This is why precision medicine has emerged as a promising direction for biomedical research in recent years. Precision medicine aims to determine optimal diagnostic and therapeutic strategies for each individual patient by taking into account their unique genetic, environment and lifestyle factors.

As I reflected on the technology we were building for women's health, it became apparent that our personalised approach could benefit numerous underrepresented and complex patient populations, like those with rare or chronic diseases. This led towards a pivot - from startups to scientific research. The capital-intensive R&D required for developing new technologies for emerging industry opportunities creates financial risk for venture capitalists, so it's difficult to get support as a startup for developing new AI-based precision medicine technologies. But I saw that we could unlock immense benefits for society in tackling this deep tech challenge, so to continue working towards this goal I decided to pursue PhD research on the topic of AI for precision medicine.

AI in precision medicine

With the emergence of large-scale health and biomedical datasets (e.g. population-wide electronic health record systems and genetics biobanks), opportunities for AI and data-driven technologies in precision medicine include:

• Integrating more data sources that better represent diverse populations

• Moving away from a one-size-fits-all approach to more personalisation in medicine and healthcare delivery (e.g., by utilising digital biomarkers derived from multi-model data sources, as we did in the Velmio pregnancy health app)

• Machine learning methods for diagnosis and treatment planning that explicitly account for differences across patients

• Advancing the fundamental understanding of biological mechanisms of disease with deep learning and other computational methods for omics data (genomics, proteomics, metabolomics, etc.)

At scale, AI/ML technologies for precision medicine can potentially benefit many diseases and patient groups (for example, by providing AI assistance to accelerate key steps in biotech/pharma pipelines, such as clinical trials). However, despite the promise, more work is needed to ensure that such AI/ML systems are fit-for-purpose. This is why my research has explored how to make AI/ML work better for individual patients, motivated by the types of health problems we were trying to solve at Velmio.

Conclusions and future directions

My journey in health AI started with building digital health apps for women's health and has now led me to conducting research that pushes the frontiers of AI for precision medicine. This has been immensely rewarding work and has helped me to better understand the synergies between research and entrepreneurship. My goal in the coming years is to further translate AI/ML research developments into practical solutions that can ultimately benefit patients and improve health outcomes.

I am always open to new collaborative opportunities and perspectives on research and business problems in digital health and precision medicine. If you are working in this space, please do connect and/or drop me message!

References

[1] Caroline Criado Pérez, Invisible Women: Data Bias in a World Designed for Men

[2] Kerri Smith, Women’s health research lacks funding – these charts show how

[3] Irving Zucker & Brian J. Prendergast, Sex differences in pharmacokinetics predict adverse drug reactions in women

[4] World Health Organisation, Closing data gaps in gender, March 23 2023