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-10B30
Medical conditionViral conjunctivitis
Age14 Years
Result identified
14-year-old male with viral conjunctivitis
ICD-10: B30
Present symptoms:
  • Red eyes
  • Tearing eyes
  • Sore throat
  • Cough
  • Sniffles
Additional information:
Are your eyes "glued" shut when you wake up in the morning?
Yes
Have you recently had contact with a person suffering from similar symptoms?
Yes
Are you aware that in the last 14 days you have had personal contact with a person suffering from COVID-19?
No
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 1. result
Conditions identified by XUND:
  • Viral conjunctivitis
  • Acute bronchitis
  • Common cold
ICD-10N23
Medical conditionRenal colic
Age45 Years
Result identified
45-year-old male with renal colic
ICD-10: N23
Present symptoms:
  • Flank pain
  • Nausea
  • Vomiting
  • Groin pain
Additional information:
Gently tap your flanks. Does that hurt?
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:
  • Renal pelvis inflammation
  • Renal colic
ICD-10I82.40
Medical conditionDeep leg vein thrombosis
Age65 Years
Result identified
65-year-old female with deep leg vein thrombosis
ICD-10: I82.40
Present symptoms:
  • Leg pain 
  • Swelling of the leg 
  • Difference in leg circumference 
  • Reddening of the skin 
Additional information:
Are you suffering from hypertension?
Yes
Are you aware of any heart disease?
Yes
Have you recently had a period in which you haven't moved much?
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 1. result
Conditions identified by XUND:
  • Deep leg vein thrombosis
  • Factor V Leiden Mutation
ICD-10A02.0
Medical conditionSalmonella infection
Age14 Years
Result identified
14-year-old male with salmonella infection
ICD-10: A02.0
Present symptoms:
  • Nausea
  • Vomiting
  • Diarrhea
  • Fever
  • Abdominal pain
  • Abdominal cramps
Additional information:
Have you eaten raw meat, raw fish or raw eggs in the last three days?
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:
  • Rotavirus
  • Salmonella infection
  • COVID-19 Infection
ICD-10J44
Medical conditionCOPD
Age56 Years
Result identified
56-year-old female with COPD
ICD-10: J44
Present symptoms:
  • Breathing difficulties
  • Cough
  • Sniffles
Additional information:
Have you been suffering from such complaints regularly for a longer period of time?
Yes
Do you smoke or have you been a smoker in the past?
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 1. result
Conditions identified by XUND:
  • COPD
  • Acute bronchitis
  • COVID-19 infection
ICD-10I82.40
Medical conditionDeep leg vein thrombosis
Age28 Years
Result identified
28-year-old male with deep leg vein thrombosis
ICD-10: I82.40
Present symptoms:
  • Leg pain
  • Swelling of the leg
  • Difference in leg circumference
  • Reddening of the skin
Additional information:
Are you suffering from hypertension?
Yes
Are you aware of any heart disease?
Yes
Have you recently had a period in which you haven't moved much (e.g. air travel or bed-riddenness)?
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 1. result
Conditions identified by XUND:
  • Deep leg vein thrombosis
  • Factor V Leiden Mutation
  • Vein inflammation
ICD-10J20
Medical conditionAcute bronchitis
Age61 Years
Result identified
61-year-old female with acute bronchitis
ICD-10: J20
Present symptoms:
  • Cough
  • Sniffles
  • 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 1. result
Conditions identified by XUND:
  • Acute bronchitis
  • Common cold
  • COVID-19 infection
ICD-10J02
Medical conditionPharynx inflammation
Age26 Years
Result identified
26-year-old male with pharynx inflammation
ICD-10: J02
Present symptoms:
  • Sore throat
  • Cough 
  • Headache 
  • Red throat
Additional information:
/
No
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 1. result
Conditions identified by XUND:
  • Pharynx inflammation
  • Acute bronchitis
  • Common Cold
ICD-10K37
Medical conditionAppendicitis
Age12 Years
Result not identified
12-year-old female with appendicitis
ICD-10: K37
Present symptoms:
  • Abdominal pain
  • Nausea
  • Vomiting
  • Diarrhea
  • Fever
  • Hard abdominal wall
Additional information:
Has your appendix already been removed?
No
Are you aware that in the last 14 days you have had personal contact with a person suffering from COVID-19?
No
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
  • Salmonella infection
  • COVID-19 infection
ICD-10R10.83
Medical conditionBaby colic
Age1 Years
Result not identified
1-year-old male with baby colic
ICD-10: R10.83
Present symptoms:
  • Constipation
  • Anal bleeding
  • Changed stool color
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:
  • Anal fissure

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? Here are some of the most frequently asked questions for you.

Do you work together with medical doctors?

Yes, the medical team at XUND plays a pivotal role in managing the medical database, which forms the core of our AI solution. Our medical doctors verify the quality of every single piece of information in the medical database, ensuring its accuracy and reliability. This thorough verification process relies on reference literature and real-world medical practice experience. Learn more about the work of our medical team on our Medical Quality page, or read the interview with our Head of Medical.

How do you ensure the quality of the medical content?

Our in-house team of medical doctors and editors meticulously reviews and validates XUND's medical content before release, ensuring it meets ISO 13485 & MDR standards, and guaranteeing its highest quality. Additionally, the team collaborates with the Thieme Group to create fact sheets for frequently diagnosed conditions. On top of that, we continuously verify the medical accuracy of our technology through stress tests, drawing from thousands of literature case studies. For more details on our quality assurance process, please visit our Medical Quality page.

Is all content created or reviewed by medical doctors?

Yes, XUND's content is a blend of quantitative AI analysis and qualitative medical expertise. Using AI, we have analyzed millions of medical publications, which were then reviewed by our medical doctors with both literature and practical experience. This synergy, combined with our standardized quality assurance, ensures the highest level of accuracy and reliability in our content. To gain more insight into our medical quality processes, please see our Medical Quality page.

What diseases do you cover?

Our AI has the capability to automatically analyze millions of medical publications and process data on over 4,000 medical conditions. XUND prioritizes the most common medical conditions from this dataset, totaling over 500 illnesses, to provide statistically relevant and meaningful results.

Do you do clinical safety evaluations?

We continuously test our medical knowledge base and algorithms against cases reported in journals and training materials for doctors. We deem this the most reliable and scalable way to test the accuracy of the Medical API under verified, real-life circumstances. This is also the most commonly used approach to evaluate state-of-the-art devices by researchers and thus provides the best evidence base for a clinical evaluation of XUND with performance data.

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