Series Overview: “Trust, Bias, and the Algorithm: Rethinking AI in Women’s Healthcare”
AI in healthcare could help fix gender bias but only if we stop training it on the same systems that dismissed women for centuries. Can we do better?
Series Overview: “Trust, Bias, and the Algorithm: Rethinking AI in Women’s Healthcare”
In modern medicine, trust isn’t evenly distributed. Not across gender. Not across race. Not across ability. And certainly not across the quiet, chronic, complicated conditions that don’t show up cleanly on a lab result.
For generations, women have learned to manage their healthcare defensively. To bring binders of documentation. To downplay emotion. To preemptively appear “credible.” To steel themselves for disbelief. The result? A pattern of medical neglect that isn’t accidental, it’s structural. Women are more likely to be misdiagnosed. More likely to be prescribed psychiatric drugs for physical symptoms. More likely to wait longer for pain relief. Less likely to be believed.
That isn’t anecdotal. That’s data.
Now, into this imperfect landscape enters something new: artificial intelligence.
With its promise of objectivity and efficiency, AI is rapidly being deployed in clinical settings, from radiology scans to diagnostic chatbots to hospital triage tools. For those long underserved by traditional medicine, this feels like a breakthrough. A machine, after all, doesn’t have implicit bias. It doesn’t get tired. It doesn’t dismiss you for being too complicated.
But what if that machine was trained on biased data?
What if it learned to diagnose the way the medical system already does, with all its omissions, prejudices, and assumptions intact?
This series—“Trust, Bias, and the Algorithm”—asks a provocative question:
Can AI fix the bias in medicine, or will it just automate it?
We’ll explore the tensions and opportunities AI brings to healthcare, especially for women and other historically dismissed patients. And we’ll confront the core issue at stake: trust. Not just trust in machines, but trust in systems, who builds and benefits from them, and who gets to be heard by them.
Who This Series Is For:
Patients who’ve experienced medical dismissal, particularly women, BIPOC, neurodivergent, and chronically ill individuals.
Clinicians and healthcare professionals curious (or cautious) about AI’s role in diagnosis and patient care.
AI developers and data scientists working in health tech who want to understand how bias operates outside the lab.
Policy thinkers, bioethicists, and advocates concerned with algorithmic transparency, fairness, and justice in health systems.
Anyone trying to imagine a future where healthcare is both smart and humane.
What the Series Will Cover:
The Diagnosis Delay – Why women still aren’t believed, and why AI might change that—or make it worse.
Garbage In, Garbage Out – How biased training data reproduces real-world medical harm.
Can We Build a Better Machine? – What equitable AI design in healthcare could look like.
AI You Can Argue With – Why transparency, explainability, and patient input are essential.
Beyond the Algorithm – The cultural and systemic changes needed to make any of this work.
Key Readings and References Informing This Work:
Bias in Women’s Healthcare
Doing Harm by Maya Dusenbery
Eve: How the Female Body Drove 200 Million Years of Human Evolution – Cat Bohannon
Invisible Women by Caroline Criado Perez
“The Girl Who Cried Pain” – Hoffmann & Tarzian, The Journal of Law, Medicine & Ethics
“Women and Autoimmune Disease” – Harvard Women’s Health Watch
AI, Data, and Medical Systems
Atlas of AI by Kate Crawford
“Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations” – Obermeyer et al., Science (2019)
The Digital Doctor by Robert Wachter
“Algorithmic Bias Detection and Mitigation” – Brookings Institution
“Explainable AI for Clinicians” – Nature Biomedical Engineering
Intersectional Health Equity and Trust
Medical Apartheid by Harriet A. Washington
Health Equity in a Digital World – The Lancet Digital Health
“Trust and Mistrust in the Medical System” – Pew Research Center
Black Box Medicine by Frank Pasquale
This series doesn’t promise easy answers, but it does offer a framework for asking better questions about data, equity, and whether new tools can build the kind of healthcare system that truly sees all of us.
Because if the future of medicine is algorithmic, then we’d better make damn sure it’s accountable, explainable, and built to heal; not to repeat history.
