Enormous amount of data is collected by healthcare providers around the world. This data can be leveraged with AI to bring value to every subdomain of healthcare. The key categories of applications involve diagnosis and treatment recommendations, drug research and predictive analytics in epidemiology.

Medicine research has adopted AI to aid drug discovery years ago, however implementation of AI in large scale, real-life systems is still in its infancy. This is changing rapidly, as the awareness of the importance of analytics and AI-augmentation in medicine becomes apparent. Here we present most prominent examples of using AI in healthcare.

Diagnosis and patient care

Artificial Intelligence is improving lives of patients and doctors around the globe by improving the patient scheduling, recommending most qualified and relevant doctors for given patient and symptoms. Additionally, AI can drastically increase the trust in the healthcare system by recommending a second opinion if diagnosis is unusual for these symptoms. This is done by comparing the diagnosis at hand with most likely predictions and if they are very different, the algorithm recommends a suitable doctor to ask for a second opinion.

For doctors, AI can improve the accuracy and speed of diagnosis by suggesting potential diseases for given set of symptoms and offer most informative follow-up checks to confirm the diagnosis. What’s more, AI can also suggest the most effective treatment, helping to achieve positive outcomes for more patients. This recommendation is based on historical data about symptoms, diseases, patients and treatment outcomes.

Drug research

AI is extensively used to aid drug research. Among others, AI algorithms are used to analyze small molecules and find or generate ones that are most promising for testing, and later, clinical trials. This shortens the drug development cycles significantly, by accelerating initial stages of drug discovery. Based on the outcome of initial tests AI can suggest most promising new molecules in case the tests fail, or create the testing schedule that will yield the most information about potential usefulness of a molecule as a drug. Later on, AI is used to identify best places and doctors to conduct clinical trials for rare diseases, which again shortens the drug discovery cycle.


Using weather, social media and mosquito sightings data we can predict the areas of highest risk of an epidemic outbreak. This allows to take preventive steps and allocate resources where they matter the most. If such an outbreak happens it’s possible to predict the spread of the epidemic and contain it as fast as possible. To further improve the efficiency of the containment efforts AI can be used to optimize the supply chain. By predicting estimated time of arrival of medicine and other supplies as well as optimizing the routing of vehicles used in the transportation of supplies and personnel, it is possible to respond to outbreaks faster and more accurately.

Finally, using just photos of patient faces combined with simple measurements, such as blood pressure, AI algorithms can identify diseases fast and cheap in developing areas of the world allowing to detect potential outbreaks sooner than using traditional methods.

Implementation in large scale systems

Implementing AI in larger healthcare providers is challenging. One of the main obstacles is lack of expertise in handling big volumes of data while ensuring data security and appropriate level of anonymity. As this kind of expertise is in shortage worldwide, appropriate level of funding is necessary to attract talent capable of delivering robust and performant solutions. Additionally, proper knowledge transfer between healthcare professionals and data professionals is necessary to ensure that the goals fully align with execution and final product can easily be operated by healthcare personnel, while retaining high performance.

Closing words

In summary, Artificial Intelligence is improving the lives of millions of patients and saving lives. It’s impact will only continue to grow, as more data becomes available and breakthroughs in Artificial Intelligence will allow for more and more advanced analytics. Large scale implementation remains the most important obstacle, but as healthcare providers begin to realize the importance of data-driven care, we expect to see rapid adoption of Machine Learning not only in research, but also in applied healthcare.

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