Most money in healthcare goes to treatments, however, 70% health gains may be achieved at various levels of prevention. What are they?
Precision medicine has received much attention in recent years, but precision public health is a novel field that challenges one-size-fits-all approaches on a larger scale: the population level
Digitalisation of healthcare has from the started been plagued by a variety of challenges. Here is the list of most common problems to those who want to learn from others’ mistakes.
With so much talk on problems and issues in digitising healthcare, it is useful look back and get the answer to the main question: why? What benefits do computers bring to our health?
The key concepts in health-related AI explained: machine learning, deep learning, neural networks, natural language processing and support vector machine
Governments, regulators, healthcare providers, AI professionals and educational institutions all have a responsibility to address the challenges in the adoption of AI in the healthcare sector
Various challenges exist that can undermine or limit the adoption of AI, from the lack of digitalisation in the healthcare system to issues around trust and reluctance
AI will be adopted into healthcare in three distinct phases, first addressing routine tasks before accelerating the shift to home-based care then acting as a clinical decision support
AI adoption in healthcare will benefit the public, healthcare professionals and health systems across the full continuum of care, from keeping well, through diagnosis and treatment to end of life care
The proliferation of AI adoption in healthcare is being driven by factors in and outside the health sector, from technological improvements to the increased availability of health-related data
