Medical accuracy
96,7 %

*Evaluated based on 1,471 case studies.

Medical Quality

Leading-edge medical accuracy and reliability.

We verify the medical accuracy of our technology in medical stress tests. This happens continuously and on the basis of thousands of case studies from the literature. The goal is to identify the verified diagnosis from the case study as a possible outcome. This has proven to be the most reliable method of testing medical accuracy under objective conditions.

Benchmark with case studies from the standard literature.

In order to objectively assess the quality of XUND in comparison to other solutions, we assessed the 45 case studies from Semigran et al. 2015, one of the most frequently cited publications in the domain. These case studies are regularly used by researchers to evaluate medical accuracy, most recently by Ceney et al. 2021. The results showed that 93.3% of the cases were identified correctly.

ICD-10
Medical condition
Age
Result
ICD-10 J00
Medical condition Common cold
Age 30 Years
Result identified
30-year-old male with common cold
ICD-10: J00
Present symptoms:

  • Runny nose

  • Sore throat

  • Increased sweating

  • Headache

  • Cough

  • General muscle pain

  • Limb pain

  • Red throat

  • Swelling of the lymph nodes

Additional information:
Do you smoke or have you been a smoker in the past?
Yes
Source:
Intermittent low back pain referred from a uterine adenomyosis: a case report. J Chiropr Med. 2011;10(1):64-69. https://dx.doi.org/10.1016%2Fj.jcm.2010.08.004
Verified diagnosis identified as 3. result
Conditions identified by XUND:
  • Acute bronchitis
  • Pharynx inflammation
  • Common cold
ICD-10 B27.0
Medical condition Pfeiffer's disease
Age 16 Years
Result identified
16-year-old female with Pfeiffer's disease
ICD-10: B27.0
Present symptoms:
  • Fever
  • Sore throat
  • Physical exhaustion
  • Swallowing difficulties
  • General malaise
  • Swelling of the lymph nodes
  • Red throat
  • Rash
Additional information:
Do you feel like the rash is spreading to your whole body?
Yes
Source:
Evaluation of symptom checkers for self diagnosis and triage: audit study Hannah L Semigran, Jeffrey A Linder, Courtney Gidengil, Ateev Mehrotra 2015
Verified diagnosis identified as 2. result
Conditions identified by XUND:
  • Scarlet fever
  • Pfeiffer's disease
  • COVID-19 infection
ICD-10 J02
Medical condition Pharynx inflammation
Age 7 Years
Result identified
7-year-old female with pharynx inflammation
ICD-10: J02
Present symptoms:
  • Fever
  • Sore throat
  • Nausea
  • Vomiting
  • Swelling of the lymph nodes
  • Red throat
Source:
Evaluation of symptom checkers for self diagnosis and triage: audit study Hannah L Semigran, Jeffrey A Linder, Courtney Gidengil, Ateev Mehrotra 2015
Verified diagnosis identified as 2. result
Conditions identified by XUND:
  • Tonsil inflammation
  • Pharynx inflammation
  • COVID-19 infection
ICD-10 H66
Medical condition Inflammation of the middle ear
Age 2 Years
Result identified
2-year-old male with inflammation of the middle ear
ICD-10: H66
Present symptoms:
  • Sniffles
  • Cough
  • Sleeping difficulties
  • Loss of appetite
  • Fever
Source:
Evaluation of symptom checkers for self diagnosis and triage: audit study Hannah L Semigran, Jeffrey A Linder, Courtney Gidengil, Ateev Mehrotra 2015
Verified diagnosis identified as 2. result
Conditions identified by XUND:
  • Acute bronchitis
  • Otitis media of the infant or toddler
  • Common cold
  • COVID-19 infection
ICD-10 D59.3
Medical condition Hemolytic uremic syndrome
Age 4 Years
Result not identified
4-year-old male with hemolytic uremic syndrome
ICD-10: D59.3
Present symptoms:
  • Sniffles
  • Cough 
  • Sleeping difficulties
  • Loss of appetite
  • Fever
  • Irritability
  • Runny nose
Source:
Evaluation of symptom checkers for self diagnosis and triage: audit study Hannah L Semigran, Jeffrey A Linder, Courtney Gidengil, Ateev Mehrotra 2015
Verified diagnosis could not be identified
Conditions identified by XUND:
  • Rotavirus
  • Norovirus

The knowledge of millions built into one health solution.

Step 1
Data analysis with AI

Our artificial intelligence can analyze millions of medical publications from the literature and process data on over 4,000 condition concepts.

Step 2
Review by doctors

Our team of doctors then verifies this information qualitatively and enriches it with reference literature and practical experience.

Step 3
Medical stress test

We verify our technology with thousands of real life case studies. This is the most objective way to test accuracy of our system.

Step 4
Usability tests

Through ongoing testing with real users, we ensure that our medical algorithms work as intended and meet the defined requirements.

Step 5
Release new version

We are continuously updating XUND in line with medical device regulations and to improve accuracy and coverage.

Want to know more? We have collected some of the most frequently asked questions for you.

Do you work together with medical doctors?

We have our own team of medical doctors who verify the quality of every single piece of information in the medical database based on reference literature and experience from the medical practice.

How do you ensure the quality of the medical content?

Our medical content is created and validated in cooperation with the Thieme Group, tailored to our offering. On top of that, we have our own team of medical doctors and editors who ensure we adhere to the highest quality.

Is all content created or reviewed by medical doctors?

Our technology is based on two pillars, one quantitative and one qualitative. Using AI, several million medical publications are analyzed and then reviewed again by our doctors with literature and practical experience. So it's the symbiosis of both worlds and our standardized quality assurance systems that make the difference.

What diseases do you cover?

Our AI can automatically analyze millions of medical publications and process data on over 4.000 medical conditions. However, in order to provide our users with statistically relevant and meaningful results, XUND prioritizes the most common medical conditions out of this dataset, which account for the vast majority of illness cases. Today, this is more than 500 illnesses.

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