Tempus AI: Data-Driven Precision Medicine

Tempus uses AI to help doctors and institutions to fight cancer and other diseases. The company has built the world’s largest library of clinical and molecular data and is now valued at more than $8 billion dollars.


Tempus is a Chicago-based AI company that develops precision medicine[a] solutions using patients’ multi-modal data. The company was founded in 2015 and now boasts the world’s largest library of clinical and molecular data. Tempus focused initially on fighting Cancer but is now expanding its solutions into infectious diseases and mental health.

Product offerings

Originally, Tempus had three main lines of business in a symbiotic cycle.

  1. Genomic sequencing – Tempus offers a broad range of DNA and RNA sequencing tests that Physicians can order for their patients. In this product, Tempus combines genomic data with clinical data to provide insights on gene alterations associated with specific types of tumor and enable oncologists to prioritize among different treatment options.
  2. Clinical data structuring – Tempus partners with health-care systems and academic institutions to collect, organize and aggregate clinical data primarily but not exclusively from electronic health records. The company uses image recognition, optical character recognition (OCR) and natural language processing (NLP) to transform unstructured information that is buried inside radiology scans, pathology slides, health reports and oncologists notes into structured data.
  3. Data monetization – Tempus sells curated and anonymous data to pharmaceutical companies for research and development of new drugs.

Tempus ONE

Earlier this year, Tempus announced a bold new addition to its product offering. The company launched the beta version of Tempus ONE, a voice-enabled portable smart-speaker designed for helping physicians on real-time. The device is intended to be a physical manifestation of the Tempus library. It can conveniently lie in a doctor’s desk and is small enough to be carried in the pocket.

After listening to a question, Tempus ONE searches the answer on the library, organizes the data into a clinical context, and makes it seamlessly available for doctors as they take care of patients. Tempus ONE lets doctors ask a broad range of inquiries, including questions about a patient’s condition, clinical and genomic information, and test status (e.g.: “Tempus ONE, what is Karla Sanders’ MSI status?”, “Tempus ONE, what alterations were found in Karla Sanders?”).

Watch the video below for Tempus ONE Demo:


While the advertised job-to-be-done of Tempus ONE is to answer doctors’ questions, it’s likely that Tempus intends to use the device to collect conversational data from doctor-patient interactions. This new category of data would certainly make Tempus’ library even more powerful.

Challenges and limitations

It’s true that the use of AI in healthcare enabled by large sets of historical data is very powerful and exciting. AI perhaps is the single most promising technology to improve people’s health by finding the cure for diseases, developing new drugs and vaccines, and improving therapies. However, a lot of challenges lay ahead. Below, I exemplify the most important ones:

  • Limitations because of data privacy and restricted data sharing – Data privacy is increasingly a challenge for digital companies, and health data is even more sensitive. Not only are patients usually not comfortable that others know details about their health, especially when it comes to serious diseases like cancer, but also a patient’s data can be used harmfully against the very same patient who provided it. Think of what happens if insurance companies suddenly get access to data about customers with terminal diseases, for example. For those reasons, sharing of data among health institutions is very restricted, which limits research and development efforts. Every application that uses identified patient data has be carefully thought through under ethical, privacy and legal lenses.
  • Issues with Data quality – Algorithms are only as good as the data. Inconsistences and flaws in the systems that collect, process and record data undermine data quality, which in turn makes algorithms weaker. Also, a lot of data is still not captured because it remains in analog means, like a doctor hand note or a physical x-ray sheets, or in a system that is digital yet not integrated as a source of data. Digitalization of analog data and systems integration are all subjected to failures and often add more errors to the data.
  • Lack of data for new diseases – AI algorithms rely on historical data. For novel diseases like the COVID-19, too little data is available. It takes a long time until there’s enough information to train, test and validate AI models, long enough to say that it’s likely that a pandemic like this one will come to an end before the most actionable insights can reliably be drawn from the data.

[a] Wikipedia defines precision medicine (PM) as “a medical model that proposes the customization of healthcare, with medical decisions, treatments, practices, or products being tailored to a subgroup of patients, instead of a one‐drug‐fits‐all model.” [1]



[1] https://en.wikipedia.org/wiki/Precision_medicine

[2] https://hesiliconreview.com/magazine/profile/tempus-data-driven-precision-medicinet

[3] https://www.cnbc.com/2020/06/17/tempus-ai-health-firm-backed-by-groupon-co-founder-hunts-coronavirus.html

[4] https://www.businesswire.com/news/home/20210111005654/en/Tempus-Unveils-a-Breakthrough-in-AI-for-Oncologists-Tempus-ONE

[5] https://www.fiercehealthcare.com/tech/precision-medicine-company-tempus-lands-200m-to-expand-into-infectious-diseases-mental-health




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Student comments on Tempus AI: Data-Driven Precision Medicine

  1. Tempus ONE sounds really exciting! I love the fact that the company decided to move into the hardware space. I would imagine that the journey must have been pretty difficult for them. As I was reading your post, I also expected various challenges with data privacy and data sharing. You captured those points really well. Regarding solutions, I don’t think I have seen a company that has successfully navigated through this problem yet.

  2. Another great post Marcos! It reminded me of the Watson case we had. So many challenges related to organizing data from different sources and extracting meaning to answer specific questions. Very interesting!

    What do you think Tempus AI can do differently to succeed where Watson failed?

  3. Great Post Marcos! The work that Tempus is doing sounds really exciting. I see you’ve mentioned some of the privacy concerns related to insurance companies and individual data. But I’d also love to hear what you think about tempus selling the anonymous aggregate data as this could help insurance companies decide base premiums for large demographic groups.

  4. Thank you for sharing Marcos! I wonder if Tempus can get behind the HIPAA and other regulations within the space to ever make it in broad scale clinical practice. I do not think people will easily give it up.

  5. Interesting post Marcos! I think that these type of innovations also raise significant ethical concerns, apart from the data privacy limitations you’ve mentioned. I feel that the healthcare sector is probably where we’ll see the most controversies about implementing technology in the near future.

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