Do you want to learn how to improve the adoption of AI in healthcare?

AI is often seen as a silver bullet, but in many instances AI and data based solutions in healthcare don't fully address the problem and are sometimes not clinically safe. The current adoption of AI in healthcare is at best ad hoc, with a large number of solutions stuck in the proof of concept stage, limited to certain specialities or without robust evidence of their effectiveness.

Healthcare has always faced significant challenges, only increased by unprecedented events related to the Covid-19 pandemic. AI solutions have been proven to work and add value in other industries, and it has potential to do the same in healthcare and to help address issues which face the industry.

While there are parallels between healthcare and other industries with respect to the implementation of AI solutions, the healthcare industry is unique. The nature of healthcare, and its impact on people's lives, makes certain factors more important than in other industries.

Existing guidance and manuals provide good insights, however, you have to refer to many different sources, and attempt to 'knit it all together'. This book presents you with a more holistic approach, which improves the likelihood of a successful implementation and, later, scalability; an approach which considers both the solution lifecycle and the stakeholders involved in it. This book also highlights certain enablers which need support, planning and funding at a national level. It expands on the technical and academic style of previous resources to provide you with an accessible reference guide that is practitioner and implementation focused, grounded in evidence, and brought to life by real life examples and expert interviews.

By the end of this book, you will:

• Understand the potential of AI to add value in healthcare and to improve patient outcomes
• Know where AI implementation has worked, and the lessons learned from where it hasn't
• Have a new approach to consider when designing, selecting, and deploying AI solutions in healthcare to increase the likelihood of success and adoption at scale