How a F500 MedTech Uses All of the World's Research to Innovate with GenAI
See How a Fortune 500 MedTech Firm Uses GenAI to Accelerate Scientific Breakthroughs, With Fast, Accurate AI Search 40M+ Papers Across Data Modalities & Clouds
Fast & Accurate Search Across 40M+ Docs
Organizing Scientific Research with GenAI
A leading medical technology company sought to accelerate its highly manual research process across hundreds of scientists while maintaining the highest standards of accuracy in its findings. Their work ranged from developing new medical devices to enhancing patient outcomes through innovative technologies. With highly accurate, sub-second AI Search powered by Activeloop, they reduced the research process from weeks to days.
The Challenges
As the MedTech company expanded its AI applications to explore, understand, and utilize cutting-edge medical research, they encountered three significant challenges:
- 1
Lack of Multi-modal AI Search Capabilities
Hundreds of researchers needed to efficiently search and retrieve information from over 40 million PubMed documents and proprietary research data.
- 2
Inaccurate Retrieval for AI
Traditional search methods like keyword search were highly manual, time-intensive, and yielded low-accuracy results or missed crucial connections between different, rapidly evolving datasets.
- 3
Manual, Lengthy Cross-Referencing
Manual cross-referencing of diverse data types (e.g. figures in papers and trial data) was time-consuming and prone to errors, making it difficult to draw conclusions at scale.
The Solution
The MedTech company partnered with Activeloop to implement Deep Lake 4.0, enabling enterprise AI search with multi-modal data storage and highly accurate retrieval.
The MedTech company partnered with Activeloop to implement Deep Lake 4.0, enabling enterprise AI search with multi-modal data storage and highly accurate retrieval. A key feature of Deep Lake is Deep Memory, which ensures superior retrieval accuracy through multiple indexes, including embedding with quantization, lexical, and inverted indexes. In addition, Deep Lake allows rapid search on object storage with minimal caching, ready for advanced neural search technologies like ColPali. The resulting solution, a research chat interface, was able to quickly deconstruct the query, identify documents that are relevant to it, and provide a comprehensive, accurate answer with citations to any question asked.
The solution leverages Intel’s advanced hardware architecture to deliver exceptional query performance. 5th Gen Intel® Xeon® processors facilitate real-time embedding inference, Intel® oneAPI Math Kernel Library (oneMKL) enhances cosine similarity computations, and Intel® Gaudi® 2 accelerates batch inference for over 40 million embeddings, delivering sub-second (0.0243 seconds) performance on complex research questions.
Fast Search Across Vast Evolving Scientific Data
Enables Rapid Access To Millions Of PubMed Documents And Proprietary Research, Allowing Researchers To Stay Up-To-Date With The Latest Scientific Advancements.
Highly Accurate Answers To Niche, Analytical Queries
Utilizes Advanced AI Techniques Such As Hybrid Retrieval, Multiple Indexes, And End-To-End Neural Search, Ensuring Precise Responses For Complex Scientific Inquiries.
Fast Time-To-Market
The Research System Reduces Research Timelines From Months To Days, Significantly Accelerating The Development Of New Medical Technologies.
"This end-to-end neural search capability has fundamentally transformed how we make scientific discoveries. Our researchers can now instantly analyze connections across multiple modalities of scientific data—from medical device designs to clinical trials—and surface insights that would have taken months to discover manually. The analytical depth and accuracy we’re achieving through this AI-native approach is not just incremental—it’s enabling entirely new methods of conducting research."
Fortune 500 MedTech
VP of Research
Results
Researchers now have instant access to relevant literature across public and private knowledge bases, enabling rapid search across vast, rapidly evolving scientific data. This access allows them to stay up-to-date with the latest scientific advancements. Complex technical queries receive highly accurate responses, utilizing advanced AI techniques such as hybrid retrieval, multiple indexes, and end-to-end neural search. Diverse medical data types are cross-referenced automatically, making research more efficient.
As a result, research timelines have been compressed from months to days, significantly accelerating the development of new medical technologies and drastically increasing productivity.
4x Faster Computations, up to 7% More Accurately
in streaming cosine similarity computations. Additional Accuracy Gains Achieved by Native Hybrid Retrieval Methods.65% Faster Data Ingestion
Improved Accuracy in Predicting Potential False Positives and False Negatives, Ensuring Safer and More Reliable Robot Navigation.Sub-Second Queries
The Query Response Time Reduced to 0.0243 Seconds.9.7x Faster Time-to-Market
Research Projects That Previously Required Months to Complete Reduced to a Matter of Days.Future Plans
: From Copilot to Autonomous ResearchersThe company and Activeloop are eager to expand the system’s capabilities by integrating additional biological data types, thereby creating a more comprehensive and interconnected knowledge base. This integration will be complemented by enhancements in semantic querying to improve accuracy and provide context-aware retrieval, while automating cross-referencing across multiple data modalities for seamless and in-depth insights. These efforts will be supported by continuous improvements in retrieval accuracy, achieved through AI model fine-tuning and hardware optimizations. Finally, the development of multi-modal AI agents will empower researchers to autonomously execute extended research tasks, enabling them to generate insights over longer periods and from more complex datasets, ultimately creating a powerful, interconnected research ecosystem.
Powered by Intel
The Intel Rise and Intel Disruptor Initiative programs further bolstered the collaboration between the MedTech company and Activeloop. Intel technology was used at multiple stages of the project, including feature extraction and processing large batches of data. 4th Gen Intel® Xeon® processors, powering Activeloop’s Tensor Query Engine, facilitated quick and efficient queries across millions of scientific documents, significantly shortening innovation cycles.
As the MedTech company continues to innovate and push the boundaries of AI in healthcare, Activeloop’s Deep Lake, powered by Intel technology, remains an integral part of its AI tech stack.
Overall, this collaboration demonstrates how combining cutting-edge technology with heavily regulated industry needs can deliver substantial impact. The partnership promises to redefine how AI enables medical research, with the MedTech company leading the charge.
Fortune 500 MedTech
VP of Research
“GenAI opens up exciting opportunities in scientific research. However, in highly regulated industries like MedTech, a robust data and hardware foundation is essential for developing compliant, traceable, and accurate AI solutions. The collaboration between Intel, Activeloop, and a Fortune 500 MedTech company achieves just that—leveraging Intel's latest AI hardware alongside AI-based software assets. Activeloop’s Enterprise AI Search, powered by Intel's hardware, enables next-generation research workflows for MedTech companies.”
Chris Gough
Head of Strategy, Intel Health & Life Sciences
Intel Disclaimers
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