Research
Building from evidence-informed foundations
Meia Lab draws on established women's health research, clinical communication practice, and responsible AI design. We are early in our journey and committed to documenting our research approach as the platform evolves.
What shapes our work
Women's health data gaps
Women's symptoms are often under-recorded, under-studied, or studied through models that do not reflect lived experience. Meia Lab is being built with awareness of these structural gaps.
Symptom clustering and longitudinal tracking
Connected symptoms over time may reveal patterns that single-visit assessments miss. Structured daily inputs and signal grouping are central to how Meia Lab approaches this problem.
Midlife and hormonal transition complexity
Perimenopause, cycle changes, and hormonal transitions can produce overlapping experiences that are difficult to name. Meia Lab aims to help women organise that complexity without oversimplifying it.
Patient preparation and clinical communication
Clearer patient narratives can support more productive clinical conversations. Meia Lab explores how structured summaries and appointment briefs may help without crossing into clinical decision-making.
AI safety and non-diagnostic design
AI-supported health tools carry responsibility. Meia Lab is designed to remain non-diagnostic, transparent about limitations, and oriented toward user agency rather than automated conclusions.
How we think about evidence
Meia Lab does not present itself as a peer-reviewed medical product. We aim to be transparent about sources, limitations, and the difference between pattern interpretation and clinical conclusion. As our work matures, we will continue to refine how we reference evidence and document product decisions.
Publications and materials
Formal Meia Lab research publications and advisory materials will be added as they become available.