Combining Artificial Intelligence (AI) with medical data science is not new; research on this subject had already started in the 1960s. What is new however, is that we now have finally reached that point in time where computing power for data collection and -processing is so widely available, that the technology of AI itself becomes widely available. Together with the implementation of electronic health record (EHR) systems, which are crucial for the digitalization and information spread in the healthcare sector, we now also have the data to train the AI algorithms on.
In the recent past the first implementations of AI have made its way into the clinic, such as in diagnosis prediction and image segmentation. Besides that these implementations can be improved and expanded, there are far more applications available where AI can support the clinician, either helping them in their decision-making, or automating repetitive tasks that have a relatively small effect on patient well-being. Think about reducing the administrative workload of physicians, the automatic generation of material compositions for specific implementations in the human body, the preventive diagnosis of patients, or aiding physicians in the prioritization of their work based on an algorithm scanning for malignancies.
With the AI for Health track of FruitPunch AI, we want to speed up the implementation of AI in the healthcare sector and improve the effectiveness of the entire patient treatment process.
Focus points of the project
Our first challenge comes from the MUMC in Maastricht where a team from FruitPunch AI will aid the radiology department in relieving the workload for radiologists as a consequence of the large amount of COVID-19 patients.
Develop an AI-based tool for single to dual energy conversion of thoracic CT images, to aid in the diagnosis and monitoring of suspected COVID-19 patients and other malignancies.
Steps to take are:
🍉 Understanding the consequences of training an algorithm on patient data and requirements on algorithms that are implemented in the clinic
🥝 Exploring the different machine learning frameworks
🍒 Training of an AI algorithm using one or multiple machine learning frameworks and evaluating the performances
🥥 World Health Statistics 2018 show that still less than half of the people in the world get all of the health services they need and that millions of people are still pushed to extreme poverty due to high healthcare costs. 
🍉 Physicians spend nearly half of their time on electronic health records (EHR) and desk work. Even during examinations, EHR and examinations take up to 27% of their time spent. 
🍊 47.2% of countries and territories in the world have less than one physician per population of 1000 people. 
🥝 In 2015, 34.8 billion defined daily doses, i.e. a single antibiotic capsule or injection, were administered. Antibiotics have increased the quality of life ever since they came into existence, however exact doses differ per patient which can lead to additional side-effects. Moreover the increase of (unnecessary) antibiotic prescriptions is leading to increasing resistance to antibiotics.