Speaker Profile
MSc Oncology and Computational Biology, Biomedical Scientist, Pangaea Data
Biography
Mr. Marcin Burda, Biomedical Scientist at Pangaea Data, is a passionate scientist and entrepreneur. With a background in academia, Marcin has specialized in cancer research and, prior to Pangaea, he worked in biotech on a team developing a groundbreaking immunotherapy. As the leader of Pangaea's biomedical team, Marcin works closely with Pangaea's customers and partners which has enabled him to successfully secure and deliver on several deals with global pharmaceutical companies looking to increase their revenues and improve patient outcomes through the application of Pangaea's product. Marcin takes an active role on the front-lines of Pangaea's USA and EU-UK business, currently managing projects worth $10 million, while helping identify new clinical pain points with prospective customers, converting these into further opportunities for Pangaea's product to benefit clinicians and patients a-like.
Talk
Crafting Personalized Care with AI and Data
Learn how NHS clinicians applied Pangaea to find and connect to 6x more undiagnosed and miscoded cachectic cancer patients compared to generic natural language processing (NLP) and ICD approaches. This led to a 50% reduction in treatment costs resulting in annual savings of 1B for the NHS coupled with improved resource allocation and patient outcomes.
AI and Data Sciences Showcase:
Pangaea Data
Pangaea’s product platform combines clinical guidelines and AI to find and connect to more undiagnosed patients for 7,000 hard-to-diagnose conditions across 140,000 healthcare providers globally. This is improving patient outcomes, halving treatment costs and enabling privacy-preserving and scalable collaboration between pharmaceutical and healthcare providers.
Session Abstract – PMWC 2024 Silicon Valley
The PMWC 2024 AI Company Showcase will provide a 15-minute time slot for selected AI companies to present their latest technologies to an audience of leading investors, potential clients, and partners. We will hear from companies building technologies that expedite the pre-clinical and clinical drug discovery and development process, accelerate patient diagnosis and treatment, or develop scalable systems framework to make AI and deep/machine learning a reality.
