Meet the platform

Infermedica improves the diagnostic process using the most advanced reasoning technology for preliminary medical diagnosis.


White-labelled enterprise platform (web, mobile, chatbot or voice-enabled applications) for symptom checking, patient triage and clinical decision support.

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Medical API

Open developer portal that allows to easily integrate our diagnostic engine into 3rd party software.

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The complete technology

Infermedica collects, analyzes and uses medical knowledge to ask diagnostic questions to unmask your patients' conditions.


We collect and organize medical knowledge.

Machine learning

We analyze literature and patient records.

Inference engine

We draw conclusions from patient data.


We integrate with your product smoothly.

Artificial Intelligence

Benefit of an evidence-based medical knowledge base

Infermedica’s medical knowledge is stored in a proprietary database of probabilistic relationships between such various diagnostic variables as conditions and symptoms.

Verified knowledge

We rely on solid medical information from well-established publications.

Statistical facts

We extract the information from millions of anonymized patient records.

Human experts

We have the medical content curated by a board of expert physicians.

Reveal the power of machine learning

Our platform becomes smarter with use. We apply machine learning and data extraction algorithms to expand our diagnostic capabilities.

  • Collected data

    We find dependencies and common patterns in patient data.

  • Literature

    We extract knowledge from literature and published medical cases.

  • External sources

    We learn from your custom data sources, e.g. anonymized EMR records.

Arrows point the digital brain

Get reliable diagnostic insights

Our inference engine utilizes unique and the scalable data modeling approach based on Bayesian Networks. It is designed to resemble the reasoning of expert level human diagnosticians and guarantees an unmatched query response time.

Ask relevant questions

It creates interactive, dialogue-based apps with our adaptive question selection algorithm. Our algorithm can change diagnostic hypothesis at any given point if new evidence suggests a better fit.

Learn about likely diagnoses

Our inference algorithm provides a probability-based ranking of likely conditions, suitable for patient triage, symptom checking and decision support systems.


Where do you get the data?


Our medical data is initially created by a team of experienced physicians based on respected literature, the latest research articles and their own experience. However, we also have a complete authoring tool that allows the customizing and expansion of the medical content to solve various diagnostic problems.

How reliable it is?


Infermedica has been successfully used in a number of commercial products and has already handled millions of diagnostic requests through its API. Thanks to its machine learning capabilities, Infermedica becomes smarter every day since it incorporates the data it analyzes.

How much does it cost?


The licensing fee depends on the scope of our collaboration. If you wish to contribute valuable input to our medical knowledgebase, we will probably give you favorable terms for the use of our technology. Feel free to contact us with any questions.

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Learn how others use Infermedica

Here are some use cases that show how our technology can be used to build more powerful medical applications.

HealthLoop screenshot


Infermedica is used to provide adaptive and personalized interviews with patients on possible post-surgical complications.

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Symptomate screenshot


Infermedica is responsible for assessing risks of primary care conditions with one of the leading symptom checkers.

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DxMate screenshot


Infermedica is used to generate a set of conditions to consider based on entered symptoms and to provide insights on relevant diagnostic observations to verify.

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Learn more about Infermedica

Join us for a live demo. We’ll walk you through our products and answer any questions you have.

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The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 762032