Since the introduction of Electronic Health Records (EHRs), there have been large databases of information on each patient, which collectively can be used to identify healthcare trends within different disease areas. The EHR databases contain the history of hospital visits, reports of diagnoses and interventions, lab test, medical images, and clinical narratives.

For millennia individuals relied on physicians to inform them about their own bodies and to some extent, this practice is still applied today. However, the relatively new field of wearables is changing this. Wearable health devices (WHDs) are an upcoming technology that allow for constant measurement of certain vital signs under various conditions.

Augmented and virtual reality (AR and VR) are most beneficial when implemented at the early stages of education for medical students, especially those training for a specific specialty and experienced surgeons.

Machine learning (ML) is a subfield of artificial intelligence (AI), defined in the 1950s by AI pioneer Arthur Samuel as “the field of study that gives computers the ability to learn without explicitly being programmed.” 

Interpretation of data that appears in the form of either an image or a video can be an onerous task. Experts in the field have to go through extensive training for many years to attain the ability to configure medical data and on top of that have to actively keep up-to-date with new information as more research emerges.

The costs of bringing a new drug to market is a long, costly, and complex process that can often take more than 10 years from identification of molecular targets until a drug product is approved and marketed.

The benefits of exploiting AI applications for healthcare purposes are substantial. AI is ready to support the healthcare system with a variety of tasks including administrative workflow, clinical documentation and patient outreach, as well as highly specialised support such as in image analysis, medical device automation, patient monitoring, dosage error reduction, and cybersecurity.

Research in AI for various applications has been ongoing for a number of decades. As a result, there have been rapid technological advances within the field of AI and data science. This is predicted to make a paradigm shift in the integration of AI technology and its adoption and impact on society. The combination of increased computer processing speed, big data libraries, and an increasing population of AI talent has enabled the rapid development of AI technology, specifically within healthcare.

Big data and machine learning are having severe disruptive powers on the more traditional processes across most aspects of modern life. All this data is the ebb of the digital revolution. It could be of great value for behavioural understanding and tailoring products and services to the needs of consumers. But the value of artificial intelligence in the service of society can be better understood through the digital transformation and the opportunities it is about to unleash in the healthcare system.