Software companies often overlook that they operate a supply chain: including a software agency to write code, a cloud supplier for hosting, open-source libraries in your codebase, an underlying operating system, external QA services, cybersecurity utilities, regulatory advisors, and data providers for training or validating AI models. Each of these can directly impact the quality of your SaMD.

Software and AI medical devices (AI/SaMD) are solving clinical problems at speed. Healthcare systems, though, won't adopt a technology that works brilliantly if they can't make an economic case for it. At the Hardian Summit 2026, we hosted a workshop on Health Economics and Outcomes Research for professionals new to the field.

From AI regulation to NHS adoption, here are the biggest insights from Hardian Health Tech Summit 2026 and what they mean for HealthTech companies.

A strong literature review shapes SaMD study design, from comparators to sample size. Learn how it reduces risk in pivotal clinical studies.

Science washing in digital health can damage trust and trigger regulatory scrutiny. Learn how to communicate evidence responsibly and build credibility.

SaMD regulatory requirements are fast evolving, with international efforts to harmonise frameworks still taking shape. Yet manufacturers must navigate jurisdiction-specific rules—especially as the US aligns more closely with ISO 13485:2016 in its Quality System Regulation. Whether you're targeting EU, US, or emerging markets, understanding the SaMD regulatory landscape is a critical first step.

Do AI medical devices need clinical data for CE mark? Many teams building AI that predicts, measures, prioritizes, or triages ask whether prospective clinical data is required under the EU MDR. Dr Ankeet Tanna outlines when clinical investigations are essential, when indirect clinical benefit and non‑clinical evidence can be enough.