What’s the Future of AI in Healthcare?

Artificial intelligence is becoming increasingly present in modern business and everyday life. It is also being steadily applied to healthcare. 

Using AI in healthcare can assist healthcare providers in several different aspect of patient care and administrative processes, allowing them to improve upon the already existing solutions and overcoming challenges more quickly.

The vast majority of AI technologies used in healthcare have strong relevance to the healthcare field, but the tactics that they support can have significant variances between hospitals and other healthcare organizations. 

While some articles of AI in healthcare may suggest that using AI in healthcare is capable of performing just as well if not better than humans when it comes to certain procedures, such as disease diagnosis, it will be a considerable number of years if ever before AI in healthcare can replace humans for a wide range of medical tasks.

It is still unclear for many. What is AI in healthcare? What are the benefits of AI in healthcare? How is AI used in healthcare today and how will it likely be used in the future? Is it likely to replace people in key operations and medical services someday?

Let’s look at some of the different types of AI and the benefits that the healthcare industry can derive from their use.

Machine Learning

One of the most common forms of AI in healthcare is machine learning (ML). It is a broad technique that’s at the core of many approaches to AI in healthcare and there are actually multiple versions of it.

Precision medicine is perhaps the most commonly used application of traditional machine learning. Being capable of predicting which treatment procedures are likely to be successful with patients based on their make-up and treatment framework is a massive leap forward for many healthcare organizations.

The majority of AI technology in healthcare that uses precision medicine and machine learning applications requires data to conduct training, for which the end result is already known. That’s referred to as supervised learning.

AI in healthcare that uses deep learning is also used for speech recognition in the form of natural language processing (NLP). Deep learning models have features that typically have little meaning to human observers, which means that the results of the model may be challenging to delineate without the right interpretation.

Natural Language Processing

One of the goals of AI technology in healthcare for over 50 years has been to make sense of the human language. The vast majority of NLP systems include forms of text analysis or speech recognition followed by translation.

AI in healthcare is commonly used in NLP applications capable of understanding and classifying clinical documentation. The NLP systems are capable of analyzing unstructured clinical notes on patients, providing valuable insight into understanding quality, improving methods, and ensuring better results for patients.

Rule-Based Expert Systems

In the 1980s and later periods, expert systems based on different variations of ‘if-then’ rules were the prevalent technology for AI in healthcare. AI is regularly used in healthcare to this day to provide clinical decision support. The vast majority of current EHRs (Electronic Health Record Systems) make available a set of rules with their software offerings.

Expert systems typically involve human experts and engineers building an extensive set of rules in a particular knowledge area. The systems function well up to a point and are not only easy to follow but also to process. However, as the number of rules grows larger, usually exceeding several thousand, those rules may start conflicting with each other and falling apart.

Furthermore, if there are significant changes in the knowledge area, it can be both laborious and burdensome to change the rules. Rule-based expert systems are slowly being replaced by machine learning in healthcare with approaches that are based on the interpretation of data using advanced medical algorithms.

Diagnosis & Treatment 

Diagnosing and treating diseases has been at the core of AI in healthcare for the past 50 years. Early rule-based systems were capable of accurately diagnosing and treating disease but weren’t completely accepted for clinical practice. They weren’t significantly superior at diagnosis compared to humans and the integration with health record systems and clinician workflows was less than ideal.

But whether algorithmic or rules-based, the use of AI in healthcare for diagnosis and treatment is often challenging to marry with EHR systems and clinical workflows. The challenges with integration have been a major barrier to the widespread adoption of AI in healthcare in comparison to the accuracy of suggestions.

Most of the AI technologies used in healthcare for diagnosis and treatment from the vendors of medical software are standalone and address just a certain area of care. Some EHR software vendors have started building limited healthcare analytics functions with AI in their product offerings, but those are still in the elementary stages.

To take full advantage of AI technology in healthcare using standalone EHR systems, providers will actually either have to take on substantial integration projects or use third-party vendors with AI capabilities and are able to integrate with their EHR.

Administrative Applications

AI in healthcare has several administrative applications. The usage of AI in hospital settings is much less game-changing here in comparison to patient care. Still, AI in hospital administrative areas can provide significant efficiencies.

AI in healthcare can be used for several different administrative applications, which include but aren’t limited to claims processing, clinical documentation, medical records management, and revenue cycle management.

The other use of AI in healthcare that’s applicable to claims and payment administration is machine learning that can be used for data pairing across different databases. Insurers and providers are required to verify whether the millions of claims submitted each day are correct. The identification and correction of issues with coding and incorrect claims helps all parties save money, time, and resources.

What’s the Future of AI in Healthcare?

The biggest challenge to AI in healthcare isn’t whether the technologies will be capable enough to be useful, but rather ensuring that they are adopted in daily clinical practice. In time, clinicians may migrate towards tasks requiring unique human skills, tasks requiring the highest level of cognitive function. 

The only healthcare providers likely to lose out on the full potential of AI in healthcare could eventually be those that refuse to work alongside it.