Artificial Intelligence or also known as AI is a term used to describe the use of machine-learning algorithms and software to mimic human cognition in the analysis, presentation, and comprehension of complex medic and healthcare data. AI specifically has the ability of computer algorithms to approximate conclusions based solely on data inputted in them.
The emergence of AI being used in healthcare has been groundbreaking, making diagnostics and treatment of patients more accessible, specially on monitoring the patients. This technology has helped a lot in diagnosing and improving healthcare research, thus, producing more accurate diagnoses and giving more personalized treatments to patients. AI also helps medical professionals identify disease markers and trends that are usually overlooked in the past.
This technology also helps hospitals and clinics become smarter, faster , and more efficient in providing care to millions of people worldwide.
With the demand of this technology in our healthcare systems, there are already different types of AI that are being used as of today. Below are a few examples and how our healthcare industry can benefit from their use.
1. Machine Learning- this is one of the most common examples of artificial intelligence and healthcare working together. Machine learning algorithms can quickly process large amounts of clinical documentation, identify patterns and make predictions about medical outcomes with greater accuracy than ever before. The most widespread utilization of traditional machine learning is precision medicine. It predicts what treatment procedures are likely to be successful with patients based on their make-up and treatment framework.
2. Natural Language Processing (NLP)- this is a form of AI that enables computers to interpret and use human language. NLP is being used in a wide range of health data applications, such as improving patient care through better diagnosis accuracy, streamlining clinical processes, and providing more personalized services. NLP is proving to be an invaluable tool in healthcare by allowing medical professionals to use AI to more accurately diagnose illnesses and provide better personalized treatments for patients.
3. Rule-based Expert Systems- these AI systems were prevalent technology in healthcare in the 80’s and later periods. The use of AI in healthcare is widely used for Clinical Decision Support to this day. And many EHRs (Electronic Health Record systems)currently make available a set of rules with their software offerings. This technology entails human experts and engineers to build an extensive series of rules in a certain knowledge area. Machine learning in healthcare is slowly replacing this technology with the approach based on interpreting data using proprietary medical algorithms.
4. Diagnosis and Treatment Applications- this has been the core of using AI in our healthcare systems for the past 50 years.
5. Administrative Applications- the use of AI in our healthcare systems has changed many of the administrative aspects of medical care. Because of the automation of the data entry in our systems, AI has helped free up time for our healthcare providers and healthcare organizations to focus on patient care and revenue cycle management. AI has also the potential in reducing human error by providing a faster way to review health records, medical imaging, claims processing and test results.
There are challenges to adopting AI in our healthcare systems, including having to meet regulatory requirements and overcoming trust issues with machine learning results. Despite these challenges, bringing AI and machine learning to our healthcare industry has brought numerous benefits to healthcare organizations and those they serve alike. It has improved their operations by streamlining workflows and helping users to quickly find answers to their pressing questions, leading to better experiences for patients, members, citizens and consumers.