Mount Sinai Uses AI Platform to Accelerate Genomic Testing and Cancer Care Workflows

The deployment was announced at the American Association for Cancer Research annual meeting in San Diego, where Sophia Genetics confirmed the rollout of its Sophia DDM platform within the health system’s oncology and pathology workflows.

Mount Sinai Health System has begun implementing a cloud-native artificial intelligence platform designed to enhance the speed and efficiency of genomic testing in cancer care, in collaboration with Sophia Genetics.

The deployment was announced at the American Association for Cancer Research annual meeting in San Diego, where Sophia Genetics confirmed the rollout of its Sophia DDM platform within the health system’s oncology and pathology workflows.

The platform integrates AI-driven analysis of complex genomic and multimodal data, enabling faster interpretation of cancer-related genetic information. According to the company, the system is designed to deliver quicker test results and improve the accuracy of genomic insights used in precision oncology.

Mount Sinai, a National Cancer Institute-designated Comprehensive Cancer Center, supports care for more than 4,000 oncology patients annually. The system said the AI integration is helping reduce bottlenecks in molecular pathology processes and improving turnaround times for diagnostic testing.

The platform also connects Mount Sinai’s molecular pathology teams to a decentralized intelligence network spanning more than 800 global institutions. This allows clinicians to compare genomic variants against broader international datasets to improve interpretation of cancer-related mutations.

John Carey, managing director of North America at Sophia Genetics, said the platform provides “deeper genomic understanding” through AI-powered analytics, supporting advancements in precision oncology.

Jane Houldsworth, director of molecular oncology pathology at Mount Sinai, said the system has reduced hands-on analysis time and improved testing efficiency, enabling clinicians to deliver care more quickly.

The adoption comes amid ongoing concerns about pathology workforce shortages, which have been widely reported as a constraint on diagnostic capacity and turnaround times. Industry surveys have shown that staffing gaps continue to affect lab performance and service delivery.

Healthcare providers are increasingly turning to AI and automation tools to address these challenges and improve precision medicine outcomes. Experts note that digital pathology and AI-based systems are expected to further reduce delays in biomarker identification and enable faster treatment decisions.

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