Avey research

Leveraging AI to
empower health

Avey: An Accurate AI Algorithm for Self-Diagnosis

Avey was thoroughly tested internally over thousands of medical cases and extensively validated externally by independent and experienced physicians.

Our research team

Dr. Mohammad Hammoud

Dr. Mohammad Hammoud is the Founder & CEO of Avey.
He has a broad interest in computer systems and artificial intelligence applications, especially in the medical field.

Dr. Mohammad Hammoud

CEO

Shahd Douglas

The team & I are those nerds who gather health data and transfer it into simple health content. We strive to ensure the highest accuracy in the information we provide.

Shahd Douglas

Director of Health Content

Dr. Alma Dzharkas

Helping in enhancing Avey’s knowledge & ability to diagnose accurately. AI is the future of medicine & science, and Avey’s role in healthcare will be limitless.

Dr. Alma Dzharkas

Medical Doctor

Dr. Mohamad Darmach

I work on Avey himself, helping him to get the most accurate medical data to enable him to provide a diagnosis with the maximum level of accuracy.

Dr. Mohamad Darmach

Medical Doctor

Youssef Kanbour

Swiss army knife
[juːseɪf kænbaʊr] noun

The Swiss army knife is useful, multi-purpose, and adaptable.

Youssef Kanbour

Senior Software Engineer

Swapnendu Sanyal

Working on the core AI-powered diagnostic algorithm of Avey. Ideating on how to make Avey a better doctor for you.

Swapnendu Sanyal

Senior AI Engineer

Our open clinical benchmark vignette suite

We developed and peer-reviewed 400 clinical vignettes, each approved by at least 5 out of 7 independent and experienced physicians. To the best of our knowledge, this yielded the largest benchmark vignette suite in the field thus far.
To advance research, facilitate the reproducibility of our studies, and support related studies, we made all our gold-standard vignettes publicly and freely available

+400

gold-standard vignettes

Digital health has
become ubiquitous

Everyday millions of people turn to the Internet for health information and treatment advice [1, 2]. For instance, in Australia, around 80% of people search the Internet for health information, and nearly 40% seek guidance online for self-treatment [3, 4]

In the US, almost two-thirds of adults search the Web for health information and roughly one-third utilize it for self-diagnosis , trying to discover by themselves the underlying causes of their health symptoms [5]

Self-diagnosis as an integral part of digital health

Medical self-diagnostic systems are increasingly becoming an integral part of digital health, with more than 15 million users per month [6] that are likely to keep growing. A recent UK-based study found that more than 70% of individuals between the ages of 18 and 39 years would use a self-diagnostic tool [7]

Nevertheless, the utility and promise of self-diagnostic systems cannot be materialized if they do not prove to be accurate. As part of Avey’s mission, we have been researching, designing, developing, and testing a self-diagnostic algorithm to empower patients for more than 4 years now.

Articles and publications

Avey: An Accurate AI Algorithm for Self-Diagnosis
Medical self-diagnosis algorithms (or symptom checkers) are increasingly becoming an integral part of digital health and our daily lives. In this paper, we present Avey, our Artificial Intelligence (AI) based symptom checker. Alongside, we propose a comprehen- sive experimentation methodology that capitalizes on the standard clinical vignette approach to evaluate symptom checkers.
Thursday, 26 May 2022
Evaluating the Accuracy of Avey: A Clinical Vignettes Study
To evaluate the accuracy of Avey, we designed a comprehensive scientific methodology that capitalizes on the standard clinical vignette approach. Delivering on this methodology, we compiled and peer-reviewed 400 vignettes with 7 external medical doctors using a super-majority voting scheme. To the best of our knowledge, this yielded the largest benchmark vignette suite in the domain. Moreover, we defined and utilized 7 standard accuracy metrics, one of which measures for the first time in the field the ranking qualities of self-diagnostic systems and doctors in generating differential diagnoses.
Monday, 30 May 2022
Avey: A Technical Bird’s Eye View
Avey is an interactive medical self-diagnosis system that has been fully researched, designed, and developed in-house. It utilizes an intelligent inference engine with three major components: (1) a diagnostic algorithm, (2) a finding (i.e., a symptom, an etiology, or an attribute, which is a feature of a symptom or an etiology) recommendation algorithm, and (3) a ranking mechanism. The inference engine taps into a highly sophisticated probabilistic graphical model, namely, a Bayesian network. The above figure demonstrates an actual visualization of Avey’s Bayesian model. The engine's diagnosis algorithm operationalizes the Bayesian model and generates after every patient’s answer (during a session with Avey) a probability for each modelled disease, conditional on the findings that have been discovered or inferred thus far.
Monday, 30 May 2022

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