**1.1 A view of AI and ML in healthcare**

AI is becoming one of the highest priorities for healthcare decision makers, governments, investors and innovators. An increasing number of governments have set out targets for AI in healthcare, in countries as diverse as the United States, China, Finland, Germany, and the UK, and many are investing heavily in AI-related research. The private sector is also playing a significant role, with venture capital funding for the top 50 firms in healthcare-related AI reaching \$8.5 billion [15]. Though the US dominates the list of firms with highest venture capital funding in healthcare AI to date, and has the most related research studies and trials, China is emerging as the fastest growing country in this field. Europe, meanwhile, benefits from the vast depot of health data collected by national health systems and has significant strengths in terms of the number of research studies, established clusters of innovation and collaborations related to AI [16].

AI applications based on imaging, are already in use in specialties such as radiology, oncology, cardiology, neurology, pathology and ophthalmology. It is expected that more AI solutions would support the shift from hospital-based to home-based care, such as remote monitoring, AI-powered alerting systems, and virtual assistants [17, 18]. Also, AI is anticipated to be embedded more extensively in clinical workflows through the intensive engagement of professional bodies and providers. Moreover, AI solutions are expected to emerge in clinical practices based on evidence from clinical trials, with increasing focus on improved and scaled clinical decision-support tools [19]. Advances in AI mean that algorithms can generate layers of abstract features that enable computers to recognize complicated concepts (such as a diagnosis). This enables them to learn discriminative features automatically and approximate highly complex relationships [20, 21].
