Here at the APEX of Innovation we’ve written extensively about how the healthcare sector is becoming increasingly data driven. Whether it’s using wearables to accelerate diagnosis and treatment or relying on data to deliver more personalized care, there are myriad ways in which the industry is leveraging the power of data analytics.
A recent article in Healthcare IT News examined a related trend, namely how artificial intelligence (AI) is being implemented in numerous healthcare settings today. Among the primary use cases are:
Predictive and Prescriptive Analytics
Precision medicine is a prime example of how these technologies can be deployed, where a system could be used to predict the most successful treatment based on particular attributes and context.
Robotic Process Automation (RPA)
One of RPA’s chief benefits is the efficiency gleaned from using technology to automate and replicate simple, rule-based administrative tasks. In the healthcare setting, RPA can be used for activities such as updating patient records or billing.
Natural Language Processing (NLP)
NLP supports language applications such as speech recognition, text analysis, and translation, and can be deployed to analyze clinical notes or transcribe patient interactions.
As we’ve previously discussed, computer vision can identify objects with much greater accuracy than the human eye. In healthcare, the technology can help recognize potentially cancerous lesions in radiology images, support retinal scanning, or detect a brain hemorrhage.
Of course, deploying the above and other AI technologies doesn’t come without its challenges.
Chief considerations include whether the particular tool is appropriate in the specific context and also how it can be integrated into existing workflows. In addition, hospitals and practitioners must determine whether the potential gains are worth the effort of integration. And finally, there is the question of bias and fairness because AI systems typically learn from imperfect data sets that include both human and historical bias. For more on this and the current approach to AI ethics, check out this previous APEX of Innovation post.
And head over to Healthcare IT News for more on healthcare-specific solutions for ensuring AI ethics and safety.