AI can improve efficiency for healthcare providers by automating repetitive tasks and making them more convenient for the patient and doctor. In 2018, the US Food and Drug Administration (FDA) approved the first AI medical device to detect diabetic retinopathy, which is the most common cause of vision loss among diabetic patients. This device is able autonomously to detect disease on retinal images, with a sensitivity and specificity comparable to that of human clinicians. This reduces the burden for ophthalmologists to read and interpret these images.

AI can also help in triaging patients and providing diagnostic advice on common ailments without human interaction. This potentially saves money and produces better health outcomes for patients. For example, Babylon Health in the UK has developed an AI system that claims to be capable of reasoning on a space of >100s of billions of combinations of symptoms, diseases and risk factors, to help identify conditions which may match the information entered by the patient, and allow them access to the right doctors through a telemedicine platform. It is currently being used by Britain’s National Health Service for some of its patients.

In research, AI has great potential in drug discovery: to shorten the time to identify new drugs. The typical success rate in traditional drug discovery is low. An AI-based approach can reveal relationships among drugs, genes, and diseases by analyzing multiple data sets including genomic, phenotypic and clinical data, and sifting through huge public life science knowledge bases. In this field, data is often too plentiful that it is difficult for the human mind to make sense of it, and that is where AI is critical. Pharmaceutical and biotechnology companies can leverage on this to find better novel targets for early-stage drug discovery. Another excellent use case for AI in research is in clinical trials. Clinical trials are expensive, time-consuming and often face problems in recruitment of patients. AI can identify the right patient profiles that would most likely benefit from a new treatment, enabling rapid patient recruitment and trial execution.

For AI to achieve its full potential in healthcare, we need to overcome several challenges, such as technological, regulatory and clinical barriers to data sharing and usage. Different sources of data are still mostly kept in silos by multiple stakeholders (insurers, pharma, hospitals, clinics) which means it remains fragmented. There are data privacy and security issues in bringing these data together. Often, health data is captured in a disorganized and unstructured manner, requiring a significant effort and cost to clean and re-organize in a meaningful way.

Another challenge is that AI tools are developed using historical data and need to prove their applicability in the real-world clinical setting. Hence, they will require prospective clinical studies to validate their effectiveness and safety, few of which have been published so far. A limited evidence base makes it more difficult for clinicians to adopt and apply them in their practice, especially since many of the algorithms are ‘black boxes’.

AI is not a panacea. AI is excellent at processing huge amounts of data but fails miserably at mimicking the care, empathy and human touch of a physician. It will enhance the capabilities of doctors rather than replace them. Thus, healthcare organizations with proper digital strategy and commitment to overcome the challenges will be able to reap the benefits of AI to generate better clinical outcomes and financial returns. Data and AI is powering the “Fourth Industrial Revolution” that is happening today and will be the key driver of innovation in healthcare in the coming years.