Company: TEM
Filing Date: 2025-02-24
Form Type: 10-K
Source: 0000950170-25-025603
Chunk: 3

Company: Tempus AI, Inc.
Filing Date: 2025-02-24
Form: 10-K
Item: Item 1
Chunk 3
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 in oncology to provide clinical trial support for pharmaceutical companies that are looking to reach hard-to-find and underserved patient populations. Our third product line, AI Applications, is focused on developing and providing diagnostics that are algorithmic in nature, implementing new software as a medical device, and building and deploying clinical decision support tools. The primary product of AI Applications is currently “Next,” an AI platform that leverages machine learning to apply an “intelligent layer” onto routinely generated data to proactively identify and minimize care gaps for oncology and cardiology patients. As this product gains adoption, we intend to leverage large language models, generative AI algorithms, and our vast database of de-identified data to develop algorithmic diagnostics designed to identify these patients earlier in their disease progression, when treatments are most effective.

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Industry Background

The Limitations of Employing Technology, Data, and AI in Healthcare and Precision Medicine

Technology has had a significant impact on almost every sector of our global economy. From the way we shop online, access information on the internet, or use GPS to navigate the world. We benefit from, and depend on, technology, data, and the vast computational and connective ecosystem that surrounds us. Yet healthcare has seemingly lagged other industries in embracing the power of technology and leveraging the ensuing computational revolution.

We believe this is changing. Recent technological advancements have facilitated the deployment of modern computational methods, such as AI and machine learning, to improve healthcare. Breakthroughs in cloud computing, imaging technologies, large language models, and low-cost molecular profiling have made it easier and more cost effective to digitize, structure, harmonize, and store healthcare data, and analyze the resulting datasets at an unprecedented rate. These developments are expediting the adoption of AI, which we believe will impact all aspects of healthcare, from clinical diagnostic testing to the discovery and development of therapeutics, to healthcare delivery more broadly.

Despite the accumulation of healthcare data, we believe the healthcare system still lacks the integrated networks and modern analytical tools necessary to facilitate data-driven care at scale. The vast majority of healthcare data created today remains locked in silos and lacks harmonization due to decentralized institutions using non-standardized methods for collecting data, in addition to a large percentage of the data being in unstructured formats like free text (such as physician progress notes) and non-digitized images (such as pathology slides). Clinical outcomes data, to the extent it even exists, often remains disconnected from diagnostic data, and traditional laboratory tests provide results that are often based only on a single data