We are beginning to see applications of artificial intelligence in healthcare that go beyond the triage nurse chatbots, disease prediction models, and drug discovery algorithms that have been common until now. A new generation of tools will enable healthcare providers to use AI to diagnose patients and improve outcomes at lower costs by streamlining processes and reducing human error.
These advances will help accelerate AI adoption in the $3 trillion healthcare industry, which is currently fragmented and inefficient. AI has the potential to lower costs for patients and make them more willing to share their health data with providers if they trust that it will not be stored at residual risk or sold to advertisers. Read on for details on where AI is being deployed in healthcare today and what we can expect from it tomorrow.
Types of Artificial Intelligence in Healthcare
Artificial Intelligence in healthcare is a collection of many technologies. Most of these technologies have immediate relevance to the field of healthcare, but the tasks and processes they support may differ. Some of the important AI technologies are described below:
1. Machine Learning
It is one of the common forms of artificial intelligence in hospitals and healthcare. Machine learning focuses on using data and algorithms to mimic the way humans learn, gradually improving their accuracy. In healthcare, the most common application of Machine learning is in precision medicine. It predicts which treatment procedures are likely to be successful with patients based on various patient attributes and treatments. The vast majority of precision medicine and machine learning applications require a training data set for which the end result is known. This is called supervised learning.
2. Natural Language Processing
NLP includes applications such as text analysis, speech recognition, and other language-related purposes. A common use of NLP in healthcare involves the creation and classification of clinical documentation and published research.
Natural language processing techniques can analyze patient clinical notes that are unstructured, providing incredible insight to improve methods, understand quality, and achieve better patient outcomes.
3. Automation of Robotic Processes
RPA uses automation technologies that can learn, mimic, and then execute rule-based business processes. Compared to other forms of AI, they are inexpensive, easy to program, and transparent in their actions. In healthcare, they are used to automate repetitive tasks like updating patient records or billing.
4. Rule-based Expert System
A rule-based expert system is the most basic type of artificial intelligence, employing knowledge-based rules to solve a problem. The expert system's goal is to convert a human expert's knowledge into a set of coding rules that can be applied to incoming data.
The Current use of AI in Healthcare
In today's world, industrial and technological revolutions are accelerated by the global application of next-generation information and communication technologies such as IoT (Internet of Things), AI, blockchain technology, etc. There are a variety of dimensions in healthcare where AI is now emerging as a game changer. Here are some highlights:
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Drug discovery: From the analysis of vast databases of existing drug information, AI-based solutions are being developed for the identification of new potential treatments and therapies. This would help to redesign existing treatment structures and medicines to overcome critical threats that have emerged in the last decade, such as the Ebola virus and the coronavirus. AI would improve the success rate and effectiveness of respective drug developments and speed up the process of introducing new drugs into the market to counter these deadly diseases.
Radiology: AI-based solutions are being developed to robotize imaging examination and diagnosis. This would help to highlight the attention areas on a body for the radiologist through a scan, and it will also bring high efficiency by avoiding any kind of human error.
The recent inventions of detecting tumors in a body through CT scans and MRIs demonstrate the growth of new forms of cancer prevention. With the speed at which AI evolution is advancing, radiology is simultaneously growing in other areas and is directly proportional to the rapidly growing computational and data power.
Medication adherence: AI-based innovations aim to improve the interaction between doctor/physician and patients, monitor medication consumption and help raise the level of adherence, resulting in better clinical outcomes and quality of life. Many AI-based applications have been created for smartphones that encourage adherence.
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These applications act as empowering agents for the patient and her family by offering relevant information of proven quality about the disease, its side effects, treatments, precautions, etc. For self-monitoring of chronic diseases like diabetes and blood pressure, AI Robot assistants are gaining a lot of popularity among diabetic patients today.
Infection rate forecasting: AI-based interventions have technology that can track the rate of spread of the virus, how it is likely to develop in the future, identify high-risk patients, and develop real-time solutions to control it. This also reduces the workload of healthcare workers and the risk to their lives, especially in times of epidemics through rapid detection of infections.
The Future Outlook of AI
In the future years, the strongest potential for AI in healthcare will be hybrid models in which professionals are assisted in diagnosis, treatment planning, and risk factor identification but retain ultimate responsibility for patient care. By lowering perceived risk, healthcare providers will be more likely to use the technology, and it will begin to offer measurable gains in patient outcomes and operational efficiencies at scale.
How USM Business Systems can help you in your AI process
As we can see, artificial intelligence and healthcare go hand in hand due to the multiple benefits that this technology offers. Despite the challenges, AI for healthcare can produce more accurate diagnoses and treatment plans and lead to better outcomes for patients overall. Therefore, all healthcare institutions need to invest in AI solutions to deliver novel experiences and excellent services to customers.
At USM Business Systems, we work with healthcare companies on different custom models based on AI and ML that help improve revenue, reduce costs, and offer a better customer experience.
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