Scaling AI in healthcare
AI devices currently being used in the healthcare sector fall into two main categories:
- Machine learning (ML) techniques that analyse structured patient data like imaging to determine the probability of disease outcomes.
- Natural language processing (NLP) methods that extract information from unstructured data like clinical notes to transform texts into machine-readable data.
For organisations that operate in the sector, AI is fast becoming a top area of growth and investment. From apps that help patients to manage care themselves to man-made pancreases that release insulin in response to changing blood glucose levels, the industry is waking up to the benefits of using artificial intelligence in conjunction with human intelligence.
The scale to many solutions is small, but the increasing adoption across public and private providers indicates that momentum is accelerating.
But what is needed for large scale adoption?
The logical starting point would be to address largely routine and repetitive administrative tasks that absorb significant time from doctors and nurses. This would help optimise operations and pave the way for widespread adoption.
Next, we would expect AI solutions to help ease the transition from hospital-based to home-based care, enabling the remote monitoring of patients with alert systems in place. But this will require a deep cultural change as the workflow of clinicians would be drastically altered.
Finally, the integration of AI solutions in clinical decision making based on lessons learned from clinical trial studies, playing an important role in how patient care is investigated and delivered.
The impact of large scale adoption will change how healthcare is delivered altogether. It will allow for enhanced patient care at every level and will help fulfil an ever growing employment gap in the sector, rather than replacing jobs completely.