## How Paige’s new Alba AI copilot revolutionizes pathology workflows
Pathology, a crucial discipline in medicine, involves diagnosing diseases and injuries by analyzing body tissue. It’s a complex and data-driven field that requires pathologists to not only examine tissue samples but also access patients’ medical records for a comprehensive understanding of their health history. To make an accurate diagnosis, pathologists must translate their findings into readable reports for other doctors and follow up with necessary actions. However, with the advent of generative AI models, pathologists now have the opportunity to be more efficient and focus on using their expertise rather than performing mundane tasks.
Paige, a 7-year-old medical technology startup based in New York City, is at the forefront of this transformation. With its proprietary AI tools and models designed for pathologists, Paige aims to revolutionize cancer research, diagnosis, and treatment. While they currently offer a tool called Alma for internal research usage at medical facilities only, the impact of their technology is undeniable.
### What Paige’s new Alba AI copilot offers
Paige’s Alba is an AI assistant or copilot that consolidates patient data from various sources like Electronic Health Records (EHRs), Laboratory Information Systems (LIS), and Image Management Systems (IMS). By aggregating this diverse data into a single system, Alba eliminates the need for manual navigation through multiple platforms, reducing administrative overhead and allowing pathologists to focus on critical tasks. With its AI-powered system, Alba summarizes patient history, prior pathology reports, radiology findings, and other key data, providing pathologists with actionable insights within seconds.
Furthermore, Alba leverages Paige’s portfolio of clinical-grade AI tools, including Paige Omniscreen, which screens molecular biomarkers in tissue samples to identify potential cancer areas. The AI system generates interim case evaluations for expert review, streamlining the process of generating diagnostic reports. Physicians can review, modify, and approve these reports using voice commands, enhancing both the speed and efficiency of the diagnostic workflow. Alba combines visual analysis with natural language processing, enabling it to write structured reports and extract relevant clinical data from electronic health records or radiology systems. This holistic approach not only assists in diagnosis but also reduces the time pathologists spend on repetitive administrative tasks.
### Proprietary in-house AI foundation models trained on millions of medical images
Paige’s Alba builds on the company’s extensive work in AI-powered cancer diagnostics. They have developed second-generation Virchow models, Virchow2 and Virchow2G, in collaboration with Microsoft. These models are part of Paige’s million-slide foundation model and have been trained on over 3 million pathology slides collected from more than 800 labs across 45 countries. The dataset includes diverse patient demographics, representing a broad spectrum of pathology use cases. With 1.8 billion parameters, Virchow2G is the largest AI model ever created for pathology.
Paige’s access to this extensive dataset through collaboration with Memorial Sloan Kettering Cancer Center in New York City gives them a significant edge in developing highly effective AI tools. Their comprehensive archive of pathology data allows them to train models capable of delivering meaningful, real-world clinical insights. By integrating the insights generated by these advanced models into real-time clinical use, Alba and Virchow2 represent Paige’s comprehensive approach to cancer care.
### Research-only for now, but a strong future for AI in clinical environments
While Alba is currently designated for research use only, its potential for improving diagnostic accuracy, particularly in oncology, signals a strong future for AI applications in clinical environments. Although it cannot be used for diagnostic procedures at present, it can help pathologists research overall cancer features and gain a better understanding of the disease. Paige’s ultimate goal is to push the boundaries of what AI can achieve in healthcare. They aim to integrate AI capabilities into clinical practice, ensuring that medical professionals have access to the best tools for diagnosing and treating cancer.
In conclusion, Paige’s Alba AI copilot is revolutionizing pathology workflows by streamlining data access and analysis, providing actionable insights, and reducing administrative tasks for pathologists. By leveraging their proprietary AI foundation models and extensive pathology dataset, Paige is paving the way for more accurate and efficient cancer diagnostics. While Alba is currently limited to research use, the future looks promising for AI applications in clinical settings. With continued innovation, AI has the potential to not only aid in cancer detection but also enhance personalized treatment plans.