Next Unicorns: Bringing AI-powered solutions to medical analysis with PathAI CEO Andy Beck | E1780

Added: Jul 20, 2023

PathAI is a research platform that utilizes AI to improve the speed and accuracy of cancer diagnosis and treatment. The platform analyzes large images of tissue biopsies to accurately diagnose diseases like cancer and predict disease aggressiveness and treatment response. While most labs still use offline microscopes, there are whole slide imaging systems available that capture digital images. PathAI has a community of 500 pathologists who help train their AI models. The company is working towards building a national database of images and collaborating with partners to generate large datasets. The ultimate goal is to provide better diagnoses and treatment options for patients. Beck acknowledges that the resistance to adopting new technology in the field of pathology is not from individual pathologists but from the challenges of implementing new systems and incentives. He believes that getting second opinions from multiple experts can improve the accuracy of diagnoses, and PathAI's platform enables collaboration and faster sharing of images. The company's AI algorithms can measure protein expression in lung cancer and help determine the best immunotherapy regimen for patients. This technology has the potential to revolutionize medical analysis and improve patient outcomes. The podcast also discusses the importance of AI in measuring and managing cancer diagnosis and treatments to improve efficacy. Biopsies help measure the effectiveness of treatments and inform future decisions. AI enables the measurement of quantitative features of images before and after treatment, providing valuable insights. Additionally, AI is crucial for diagnosing and treating diseases like liver disease. The podcast highlights the importance of screening and early detection in reducing cancer risk. However, false positives can lead to unnecessary procedures, and data collection and AI can revolutionize cancer research and treatment. There are ongoing national projects and private efforts to gather data and develop innovative solutions in this field. The discussion also touches on the topic of funding and organization in medical research. During the brain project, the three billion dollars allocated was distributed among thousands of labs, resulting in fragmented projects. Beck believes that a diversity of people attacking the problem, including those from government fund initiatives, big pharma, biotech, and startups, is beneficial. He suggests that organizing patient data at scale should be done through top-down approaches or single-payer systems, even if there is no short-term economic incentive. Beck emphasizes the significance of building a large-scale dataset for pathology image analysis. Over the years, PathAI has seen significant improvements as they have built this dataset. They have been able to develop foundation models that generalize well across different tasks. However, Beck highlights the need for a large, harmonized, and high-quality dataset to achieve statistical accuracy. He draws a parallel to self-driving cars, where data collection is focused on challenging situations and edge cases. The podcast also explores the role of compounding innovation in transforming the field of medical analysis. Beck discusses how advances in hardware, data, and software are driving these transformations. He emphasizes the democratization of access to expertise and the potential for transforming pathology and diagnostics. In conclusion, this podcast episode with Andy Beck, the CEO of PathAI, sheds light on the transformative potential of AI in medical analysis. The digitization of medical analysis, collaboration among experts, and the development of large datasets are key factors in improving diagnoses and treatment options for patients. The podcast also delves into the importance of screening, early detection, and the role of data collection and AI in revolutionizing cancer research and treatment. Overall, there is optimism about the future and the long-term impact that AI-powered solutions can have in the field of medical analysis.