There is a lot of excitement around AI in healthcare. Some of that is justified. Used properly, AI can help clinicians spot risk earlier, make better use of fragmented data, and support more consistent decision making.
But from my perspective, working at Ellescope, the most important question is not just “can the model make a prediction?”, it is “can this actually be used safely in a real clinical setting?”
That’s where healthcare AI is different from a lot of other software. The data is sensitive, the users are busy, and decisions matter. Even a technically impressive system is not useful if clinicians cannot understand it, trust it, or integrate it into their workflows.
This is especially true in maternity care, where risk is spread across different systems and teams. A patient’s medical history, psychosocial context, and support needs all matter. The challenge is not just joining that information together, but presenting it in a way that is useful for clinical decision-making.
Governance is not something that can be added at the end. For us, it has to shape how the product is built from the start. That means thinking about clinical safety, information governance, data protection, audit trails, deployment controls, and how outputs are used by clinicians.
It means being clear about the purpose of the technology. Our solution is designed to support clinical judgement, not replace it.
In healthcare, trust doesn’t come from saying a model is accurate. It comes from evidence, transparency, safety processes, and careful implementation.
That might sound less exciting than talking about algorithms, but it’s what makes the technology usable. For AI to have a meaningful role in healthcare, governance should not be seen as a blocker. It is the foundation that makes responsible innovation possible.
