- Healthcare system adopts AI different areas.
- The majority of AI and healthcare technologies are highly relevant to the healthcare industry.
- Machine learning & Natural Language Processing are the most commonly uses forms of AI in healthcare sector.
Healthcare is steadily adopting artificial intelligence (AI) technologies that are pervasive in modern business and daily life. Artificial intelligence in healthcare has the potential to help providers in many areas of patient care and operational procedures, enabling them to build on current solutions and solve problems more quickly. The majority of AI and healthcare technologies are highly relevant to the healthcare industry, yet hospitals and other healthcare organizations may employ very different strategies. And while some papers on the application of artificial intelligence in healthcare claim that it can perform particular tasks, including disease diagnosis, just as well as or even better than humans.
Let’s take a look at a few of the different types of artificial intelligence and healthcare industry benefits that can be derived from their use.
One of the most common forms of artificial intelligence in healthcare is machine learning. It is a broad technique at the heart of many approaches to AI and healthcare technology, with numerous variations.
Traditional machine learning is the most widely used application of artificial intelligence in healthcare. Predicting which treatment procedures are likely to be successful with patients based on their genetic make-up and treatment framework is a significant step forward for many healthcare organizations. The vast majority of AI technology in healthcare that employs machine learning and precision medicine applications necessitates data for training, the outcome of which is known. This is referred to as supervised learning.
Natural Language Processing
For more than 50 years, artificial intelligence and healthcare technology have sought to understand human language. Most NLP systems include speech recognition or text analysis, followed by the translation. NLP applications that can understand and classify clinical documentation are a common application of artificial intelligence in healthcare. NLP systems can analyze unstructured clinical notes on patients, providing incredible insight into quality understanding, improving methods, and better patient outcomes.
Rule-Based Expert System
In the 1980s and later, expert systems based on variations of ‘if-then’ rules were the dominant AI technology in healthcare. To this day, artificial intelligence is widely used in healthcare for clinical decision support. Many electronic health record systems (EHRs) now include a set of rules as part of their software offerings.
Human experts and engineers are typically used to create an extensive set of rules in a specific knowledge area for expert systems. They work well up to a point and are simple to follow and process. However, as the number of rules grows too large, usually in the thousands, the rules can begin to conflict and fall apart. Furthermore, if the knowledge area changes significantly, changing the rules can be time-consuming and laborious. In healthcare, machine learning is gradually replacing rule-based systems with approaches based on interpreting data using proprietary medical algorithms.
Diagnosis & Treatment Applications
For the last 50 years, disease diagnosis and treatment have been at the heart of artificial intelligence AI in healthcare. Early rule-based systems had the potential to accurately diagnose and treat disease, but they were not widely used in clinical practice. They were not significantly better at diagnosing than humans, and their integration with clinician workflows and health record systems was less than ideal.
Using artificial intelligence in healthcare for diagnosis and treatment plans, whether rules-based or algorithmic, can be difficult to integrate with clinical workflows and EHR systems. When compared to the accuracy of suggestions, integration issues have been a greater barrier to the widespread adoption of AI in healthcare. Much of the AI and healthcare capabilities offered by medical software vendors for diagnosis and treatment are stand-alone and address only a subset of care. Some EHR software vendors are beginning to incorporate limited healthcare analytics functions powered by AI into their product offerings, but they are still in the early stages.
To fully capitalize on the use of artificial intelligence in healthcare using a standalone EHR system, providers will need to either undertake significant integration projects themselves or leverage the capabilities of third-party vendors with AI capabilities that can integrate with their EHR.
Artificial intelligence has a variety of administrative applications in healthcare. In comparison to patient care, the use of artificial intelligence in hospital settings is less game-changing. However, artificial intelligence in hospital administration can provide significant efficiencies. AI in healthcare can be used for a variety of tasks such as claims processing, clinical documentation, revenue cycle management, and medical records management.
Machine learning is another application of artificial intelligence in healthcare that is relevant to claims and payment administration. It can be used to pair data from different databases. Insurers and providers must verify the accuracy of the millions of claims submitted each day. Identifying and correcting coding issues and incorrect claims saves time, money, and resources for all parties.
The greatest challenge to AI in healthcare is not whether the technologies will be capable enough to be useful, but rather ensuring their adoption in daily clinical practice. In time, clinicians may migrate toward tasks that require uniquely human skills, tasks that require the highest level of cognitive function. Perhaps the only healthcare providers who will lose out on the full potential of AI in healthcare maybe those who refuse to work alongside it.
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