Speaker Profile
Biography
Arya Khokhar, founder of Eos AI, is building the essential data infrastructure layer for reliable healthcare AI. Driven by firsthand experience of seeing millions of dollars in AI research fail to reach the clinic due to data drift, her work is focused on solving data-level generalization failures. Her mission is to make fragmented, heterogeneous medical data usable across hospitals, pharma, and research, ensuring AI systems work reliably in real clinical environments.She has led research across medical imaging, multimodal learning, and clinical data harmonization, collaborating with academic medical centers and industry partners to deploy AI systems that translate beyond single institutions. Aryas work bridges research and production, emphasizing reliability, cost reduction, and trust in healthcare AI. Her mission is to make healthcare AI-native by fixing the data foundation beneath models rather than repeatedly retraining them.
Talk
Billing to Biomarkers: Making AI That Works
AI models trained on fragmented healthcare data often fail in deployment, eroding clinician trust and slowing scientific discovery. This talk explains why current approaches don't scale, and how fixing data upstream enables reliable downstream models for drug discovery and care delivery, reducing costs, accelerating experimentation, and improving care.
AI and Data Sciences Showcase:
Eos AI
Eos AI is the data infrastructure layer that makes healthcare AI reliable in the real world. By standardizing medical images and clinical text across institutions, we enable downstream models for drug discovery, clinical decision support, billing optimization, and long-term quality-of-care improvement.
Session Abstract – PMWC 2026 Silicon Valley
The PMWC 2026 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.




