Unveiling the Role of AI in shaping the FemTech Sector

 The integration of Artificial Intelligence (AI) in women's health is a prominent trend shaping the global FemTech market. This technological change is revolutionizing how women's health issues are treated, diagnosed, and managed, delivering unprecedented efficiency and precision. In line with this, AI-driven FemTech solutions have witnessed a 50% growth in funding over the past two years, indicating a strong market interest.

These AI applications are estimated to lower healthcare costs for women by up to 20%, making quality healthcare more accessible. In terms of adoption, there is a significant 35% growth in the usage of AI-powered FemTech apps among women, particularly in reproductive health management. In addition to this, according to the research report of Astute Analytica, the global FemTech market is growing at a compound annual growth rate (CAGR) of 15.38% during the forecast period from 2024 to 2032.

The Role of AI in shaping the FemTech Sector is: -

Generative Artificial Intelligence (GenAI) is a rapidly appearing technology that is revolutionizing practically every industry and it is poised to do the exact to the femtech. GenAI can analyze vast amounts of unstructured data and identify patterns, delivering new insights and possible breakthroughs in female health.

For instance, AI can be utilized in genetic testing to deliver personalized health advice or to identify patterns indicating underlying health needs early on allowing preventative management or early treatment to enhance results.

However, the deployment of AI in FemTech is not without challenges. In the European Union, medical devices or in vitro diagnostics that include AI solutions that could impact the safety of the product are classified as 'high-risk' under the EU AI Act, necessitating additional regulatory obligations.

An algorithm disproportionately assigning wrong negatives due to bias in the underlying data or AI development methodology could drive fewer follow-up scans and potentially more undiagnosed and then untreated cases. This uses femtech solutions and diagnostics too and is due to several elements, including a historical bias in medical research, where women have usually been underrepresented, misdiagnosed, or incorrectly accounted for.

Bias may be aggravated by differences in a population or setting which serve as confounding factors not caught in the characteristics of its training environment. For instance, training data concentrated on a single hospital will account for the procedures, tools, policies, and demographic context of that hospital.

When an AI tool is released with limited training context and utilized in another hospital, the differences in patient case handling can result in a lower predictive accuracy as the model is not tuned to the hospital’s different approaches.

A large hospital in an inner city is likely to witness very different co-morbidities, patient demographics, and environmental and lifestyle factors, alongside differences in clinician specialization corresponding to a small hospital in a suburban or countryside setting – all of which can contribute to AI bias.

Both manufacturers and regulators must therefore work to ensure their AI models are developed to lower and where possible eliminate sources of bias and that an acceptable regulatory framework that accounts for the peculiarities of this new tool is developed and applied – and that appropriate methods are in place to identify confounding factors and lower bias via context-specific model tuning or advancements to underlying model as more diverse and rich training data becomes available.