Applications of Artificial Intelligence in Healthcare
Today, there are barriers to rapid implementation of Artificial Intelligence in healthcare.
To ensure widespread adoption in daily clinical practice, #AI capabilities will be required to successfully pass through regulations, system integrations, ethical considerations, legal hurdles, permissions and privacy (to name just a few).
No doubt, healthcare is a complex industry to crack!
These challenges will be overcome eventually but that will need time.
Once we steer clear through this phase, the path toward large-scale implementation will be a glorious one.
In some form and capacity, AI systems are present in healthcare today but await technological maturity and/or widespread adoption.
As a strong proponent, I am bullish about the following five:
1. Identification of treatment protocols based on patient attributes and treatment context via machine learning.
2. Detection of clinically relevant features in imaging data via image recognition.
3. Analysis of patient notes, summary reports, and patient encounters via speech recognition and text analysis.
4. Improvement in patient engagement and adherence via conversationalAI.
5. Integration with information systems to assist with repetitive administrative workflows via rule-based expert systems.
