FDI in Healthcare

Abstract

New software tools powered by Artificial Intelligence (AI) are going to be dominant components of near-future healthcare. Soon, medical practitioners and researchers will routinely adopt a wide range of machine-learning techniques in most of their daily tasks. Here, I show some of the most exciting results obtained in this space and discuss how future developments of AI will radically change the way we diagnose and cure people. Artificial Intelligence (AI) is going to heavily affect how we will research, diagnose and cure diseases in the very near future. The adoption of machine learning tools and algorithms by physicians and researchers will be of incredible benefit for the whole community. It will allow prompter and more accurate diagnoses, help doctors navigate the plethora of new medical research in order to better define their curing strategies and allow researchers to develop new cures and better identifying new patterns in complex data. Two main components are responsible for the rapid adoption of AI in healthcare: the availability of large volumes of personal health data, and the massive advancements in computing technology. Big and deep Data Until few years ago, the only way to measure most health biomarkers required systems and devices available only in the hospital environment. As a consequence, health data records in the past were obtained in a hospital environment only in cases where the actual analyses/scans were required (i.e. not in a continuous fashion all-over the daily life of the patient). But the growing use of cheap and always-recording sensors embedded on wearable smart devices and smartphones today for personal health data collection and sharing is changing this. These sensors are increasingly efficient and accurate, often competing in quality with their medical-level counterparts.The affordability of such devices and their capacity for continuously collecting data has provoked an explosion in the amount of personal-health data available to researchers and practitioners. Moreover, an easy and continuous monitoring of biomarkers outside the hospital environment can provide physicians with a much more frequent measure of their patients wellbeing. Of course, this raises a number of ethical and privacy concerns, which will be discussed later. Meanwhile, analysis and patient examination technology in healthcare facilities is becoming more detailed and able to record huge amounts of information in single scans. As an example, DNA sequencing is no more an inaccessibly expensive exam as it used to be, and even a simple blood analysis is able to return dozens of different biomarker measurements. However, when all this personal health data is aggregated in order to obtain a more complete picture of a patient's health status, the number of parameters to be studied can largely exceed the number that humans are able to evaluate. Most diagnoses are based on finding patterns in symptoms that can be linked to known diseases. The better the mapping of these symptoms, the lower the likelihood of a wrong diagnosis (assuming that the diseases'