Dr Shahnawaz Ahmed
Dr Shahnawaz Ahmed is a computer vision scientist and entrepreneur of South Asian heritage whose research specialisation in object detection and machine learning provided the technical foundations for one of the UK's most innovative medtech startups. With a PhD focused on computer vision systems, Shah built a deep expertise in the algorithmic challenges of identifying, classifying, and tracking objects in complex real-world environments — a capability that would prove transformative when applied to the surgical supply chain.
Shah's path to entrepreneurship followed his encounter with Yeshwanth Pulijala, a surgeon and researcher who had identified a critical gap in how surgical instruments were managed across the healthcare system. Where Yesh brought clinical insight and domain knowledge, Shah brought the computer vision expertise needed to build a system that could actually solve the problem at scale. Their complementary backgrounds made for an unusually well-rounded founding team.
Together, they co-founded Scalpel AI in London in 2017. As CTO, Shah led the development of the company's proprietary AI algorithms and computer vision platform, tackling the considerable challenge of building a system that could operate reliably in the demanding, highly variable environment of sterile services departments and surgical supply chains. The result was a platform that generates digital twins of surgical instruments and verifies the contents of surgical trays with expert-level precision — demonstrating a reduction in supply chain errors and handling time of over 80%.
Shah has been the driving technical force behind Scalpel AI's commercial deployments with major medical device companies, third-party logistics providers, and hospital systems internationally. The company processed over 10,000 joint reconstruction trays in its pilot with GlobalMed Logistix and closed a £3.8 million round led by Mercia Ventures in October 2024. Shah's work represents a compelling example of academic computer vision research translated into technology with direct, measurable patient safety impact.
