Fireside Chat, Panel, Other
The Next Era of Women’s Health | Future of Health Summit 2025
- New investments led by women are projected to expand coverage of fertility and menopause topics, with momentum expected to shift toward the private sector as government involvement potentially decreases.
- Social media influencers are predicted to maintain success by mirroring audience demographics and sharing stories, while medical misinformation on these platforms is expected to be addressed more effectively, though current progress remains insufficient for advancing women's health.
- Insurers are anticipated to increasingly cover menopause support across various platforms, following a recent trend where one provider announced inclusion of such support in ACA plans.
- Fibroid-related legislation is expected to advance from a partisan to a bipartisan approach, with a specific grant program aiming to fund scientists who will propose less invasive or non-invasive treatment studies.
- The medical community expects hysterectomies to transition from first-line treatment to a last resort for non-cancerous conditions, supported by a shift toward viewing fibroid surgery as non-elective due to its high prevalence relative to procedures like wisdom tooth removal.
- Screening for fibroids is expected to become standard in regular checkups for the 80% of women prone to the condition as medical professionals increasingly believe patient reports.
- Entrepreneurs and researchers are urged to focus on the "next wave" of diseases affecting post-menopausal women, including osteoporosis, hair loss, Alzheimer's, and dementia, as women are expected to become primary recipients of generational wealth while living longer.
- Digital health and AI tools are hoped to mitigate shortages in physicians, nurses, and pharmacists and reduced research funding by enabling personalized treatment plans and improving chatbot-based health information delivery through verified sources and maintained transparency.
- While AI developers are expected to be tasked with eliminating historical biases, there is uncertainty regarding whether training data will successfully be freed from male-centric bias.