What We Want Readers to Notice

What We Want Readers to Notice

Working in science and drug development, and as a woman who has needed healthcare, I have seen and experienced what happens when data practices render women a minority within the evidence used to understand and treat them. I have also seen the consequences of the medical blind spots that follow. My co-authors have encountered prejudice in their own care and have pushed for better representation in the datasets that shape research and clinical decision-making.

We wrote this chapter from three intersecting perspectives: translational science, data-enabled clinical research, and institutional operations. Across those settings, we saw the same mistake repeated. Readiness was treated as a technical threshold: enough data, satisfactory validation, functioning infrastructure, and an implementation plan.

The rising wave of generative AI—and the rapid, sometimes reckless, adoption of AI-enabled tools in healthcare—makes that mistake more consequential. A model can perform well and still fail the patient most likely to be harmed.

The chapter begins with a patient we call Elena. She is bleeding after childbirth in a busy labor-and-delivery unit. Her vital signs remain within the range the system expects. By the time the severity of the postpartum hemorrhage becomes unmistakable, she has crossed into crisis.

AI appears to offer an obvious answer: predict the risk earlier, mobilize staff and blood products sooner, and prevent escalation. But the promise of prediction can conceal a more difficult question: What happens when the records used to build the model carry the same omissions, racialized blind spots, reproductive erasures, and histories of dismissal that already shape women’s care?

An algorithm can be technically precise and still be clinically unready. It can perform well overall while failing particular groups. It can generate a risk score while ignoring staffing conditions, reported pain, reproductive history, access barriers, or the bedside judgment of nurses and patients. Once deployed, its apparent authority can make that missing context even harder to contest.

But a system is not ready merely because it can be built.

Feminist AI Readiness asks whether the women whose bodies, symptoms, histories, and risks are being modeled have real authority within the decisions being made. Can they help define the problem? Can clinicians and advocates challenge the model? Who can pause its use? What happens when a subgroup is harmed? Can consent be withdrawn? Is there a repair path with deadlines and consequences?

We wrote this chapter to move women from the position of data source to the position of governor—and to give clinical, product, and policy teams a practical way to decide when a women’s health AI system should proceed, pause, be redesigned, or never touch a patient’s life.

Johnna D. Wesley | Heather Stegner | Heather H. Ward

Johnna is a biotechnology executive and immunologist with extensive experience advancing therapies from discovery through early clinical development. Her work brings together translational science, portfolio strategy, and equitable health innovation.

Heather Stegner is an executive leader with extensive experience in strategy, operations, communications, and stakeholder alignment. She brings a systems-oriented perspective to implementation, decision authority, and the institutional conditions required to make change hold.

Heather Ward works across multi-omics data platforms and clinical translational science programs, connecting biological science, data compliance, cross-functional coordination, and operational execution. Her experience spans cardiometabolic disease, digital pathology, and machine-learning-enabled data analysis.