Potential of Artificial Intelligence in Healthcare Sector

By CIOReview | Tuesday, November 27, 2018

Artificial Intelligence combined with Machine learning and deep learning has disrupted the healthcare sector in the context of enhancing customer relations, operations and marketing, and research methods. Through this technology, computers can process a large amount of structured and unstructured data and determine patterns and predictions, thereby resulting in more informed decisions in the future.

However, machine learning requires human assistance to input the parameters for what computers need to recognize, but these inputs can be time-consuming. The next step is deep learning, now machine learning and deep learning build the basics of AI and helps in taking a holistic approach to the data being generated within healthcare. AI program can assist in detecting changes with speed and accuracy far more significant than humans. AI assistant can help to shorten the time to diagnosis while letting the clinician focus on the treatment.

Impact of AI on patient care

Artificial Intelligence has a profound effect on patient care. Faster processing speeds and models that are trained to identify things that can escape human eye have led to earlier diagnosis and improved risk identification. It has also helped in reducing cancer treatment durations, accelerated genetic analysis and faster development of customer treatment and decreasing costs. Implementation of AI includes three significant factors:

1. Descriptive analytics: This includes events that have occurred in the past and here AI detects elements which were missed by human eyes such as minor faults or abnormalities.

2. Predictive analytics: Based on the descriptive data, predictive analytics can predict any health condition which is on the verge of deterioration or development.

3. Prescriptive analytics: It provides possible treatment solutions on both descriptive and predictive analytics.

Challenges and Solutions

The quality and accuracy of the AI model solely depends upon the quality and volume of data used to train it. As with heavily regulated industries, concerns over digitization, sharing, and security of confidential and personal data are a challenge.

Any growth in the application of AI needs to come from within the medical groups and institutions and done in collaboration with the insurance providers, and also relevant government entities.