Speaker Profile
Ph.D., CEO, Diagnostic Robotics
Biography
Dr. Kira Radinsky is an accomplished computer scientist, entrepreneur, and keynote speaker. She was born in Ukraine in 1986 and moved with her family to Israel at the age of four. Radinsky showed an early aptitude for mathematics and computer science, and by the age of 16, she was studying at the Technion - Israel Institute of Technology. She was awarded the prestigious Google Europe Fellowship in Social Computing for her groundbreaking work in data mining and natural language processing and founded SalesPredict, a predictive analytics startup that uses machine learning algorithms to help businesses forecast future sales. The company was acquired by eBay in 2016, and Radinsky continued to work with eBay as its director of data science until 2017. She has since founded Diagnostic Robotics, a healthcare analytics solution company focused on using machine learning and other advanced technologies to make healthcare more efficient, effective, and accessible.
Talk
Advancing Ethical AI in Proactive Care
Care providers are constantly asked to do more with less. Carefully and thoughtfully using the latest in predictive analytics, and combining that with tools that automate manual processes (such as data mining, outreach prioritization, and information gathering) can help bring joy back to working in healthcare, address staffing challenges, and make care more proactive.
AI and Data Sciences Showcase:
Diagnostic Robotics
Diagnostic Robotics exists to bring trusted, leading edge, proactive analytics and tools to healthcare. We’re a clinician’s trusted resource to relieve administrative burdens, improve staffing ratios, reduce cost of care, and improve patient experiences.
Session Abstract – PMWC 2025 Silicon Valley
The PMWC 2025 AI Company Showcase will provide a 15-30 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.
