Leidos, University of Pittsburgh Partner on AI-Driven Diagnostics
The initiative is focused on building AI-powered diagnostics that aim to reduce turnaround time in detecting heart disease and cancer.
US-based technology firm Leidos has announced a $10 million, five-year collaboration with the University of Pittsburgh’s Computational Pathology and AI Center of Excellence (CPACE) to develop artificial intelligence tools for the early detection of critical illnesses.
The initiative is focused on building AI-powered diagnostics to reduce turnaround time in detecting heart disease and cancer. The project intends to support more timely clinical decision-making and care management by accelerating analysis.
Additionally, the partnership will fund the creation of a research hub at the University of Pittsburgh to expand CPACE’s work in digital pathology and healthcare diagnostics. It will support the development of digital pathology offerings that can be applied across both public and private healthcare sectors.
Joint Research to Improve Imaging and Diagnostics
Alongside the diagnostic tools, the two entities will undertake joint research exploring imaging technologies and medical image analysis. This includes using advanced scanning techniques and improved methods for evaluating tissue samples.
The project also integrates education and training components, offering students access to internships and symposiums involving Leidos professionals.
“Our investment is aimed at using the transformative power of artificial intelligence to speed detection, diagnosis, and treatment of diseases that affect millions of people annually,” said Tom Bell, CEO of Leidos.
“These efforts will also focus on developing future health care specialists and expanding the available care to underserved communities, including our veterans,” Bell added.
The announcement follows other recent developments from Leidos in sectors outside healthcare, including partnerships in AI for enterprise automation and contracts in defense technologies.
However, this collaboration remains rooted in clinical innovation, explicitly targeting the development of digital healthcare tools with diagnostic applications.
