AI Is Doomed Without Data

Recapping Lynx.MD CEO Omer Dror ViVE 2024 Tech Talk

Omer Dror, co-founder and CEO of Lynx.MD, presented “Data & AI: How to Overcome Data Gaps To Deliver on the Promise of AI in Healthcare.” at ViVE 2024. Dror argued that while the promise of AI in healthcare is significant, there are substantial challenges with data fragmentation, availability, and a lack of collaboration that hinder progress.

Key Takeaways

• Data fragmentation and scarcity hinder the development of accurate AI models in healthcare.
• Collaboration and data sharing across healthcare providers are crucial for overcoming this hurdle.
• Secure data-sharing platforms like Lynx.MD‘s Trusted Data Environment can facilitate collaboration while ensuring privacy.
• Widespread participation in data sharing is essential to achieve AI’s potential in healthcare and ensure health equity.

Data: The Fuel for AI Innovation

Dror highlighted the vast amount of data collected in healthcare, emphasizing that 30% of the world’s data originates from this sector. However, a significant portion of this data is unstructured and not leveraged due to fragmentation across various healthcare providers and healthcare platforms. This fragmentation makes it difficult to aggregate data for analysis and make available for AI model development.

The Challenge of Data Fragmentation

Dror explained that the fragmented nature of the US healthcare system creates challenges in collecting data across a patient’s journey. Each healthcare provider collects data specific to their touchpoint, resulting in a scattered data landscape. This fragmentation makes it challenging to create accurate predictions for various patient populations because a single healthcare system often lacks sufficient data to inform for specific conditions.

Collaboration is Key

Dror emphasized the importance of data sharing and collaboration between healthcare providers, organizations and industry. He cited research from OpenAI demonstrating the exponential increase in data size required for marginal improvements in AI model accuracy. To achieve the necessary level of accuracy for AI in healthcare, collaboration and the secure sharing of structured and unstructured data is essential.

The Role of Lynx.MD

Lynx.MD offers a Trusted Data Environment that facilitates data sharing while maintaining control and privacy. The Lynx.MD solution enables data ingestion, modeling, and de-identification to ensure secure collaboration within the healthcare ecosystem. This approach allows researchers and AI developers to access data for analysis without compromising patient privacy.

Call to Action: Collaborate with Data

Dror concluded his talk by urging the audience to participate in data collaborations. He emphasized that sharing data securely while preserving patient privacy is crucial to unlocking the true potential of AI in healthcare. Without real-world data AI is doomed to provide inadequate results. He acknowledged the challenges of data cleaning and bias inherent in data collection but stressed that collaboration is necessary to address these issues and achieve health equity through widespread AI deployment.