Big Role of Artificial Intelligence (AI) in Healthcare in 2022

Artificial intelligence (AI) and related technologies are becoming more and more common in commerce and society, and are beginning to be used in the healthcare industry. In addition to administrative procedures within providers, payers, and pharmaceutical organizations, these technologies have the potential to transform many aspects of patient care.

A growing body of research indicates that AI is currently capable of performing critical healthcare tasks including disease diagnosis as well or better than humans.

In this article, we discuss how AI will automate certain aspects of healthcare, as well as some of the barriers to its rapid adoption.

Types of AI in Healthcare

A group of technologies is collectively called Artificial Intelligence. While the majority of these technologies are immediately applicable to the healthcare industry, there is a significant range in the specific procedures and jobs they support. Define and explain some specific AI technologies that are critical to healthcare.

Natural Language Processing

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AI researchers have worked to understand human language. NLP includes applications for speech recognition, text analysis, translation, and other language-related purposes. Semantic NLP and Statistical NLP are two important techniques. Recognition accuracy has recently improved thanks to statistical NLP, which is based on machine learning (especially deep learning neural networks). It requires a large “corpus” or body of language to learn.

NLP systems are capable of conversational AI, analyzing unstructured medical notes on patients, generating reports (eg, radiological examinations), and transcribing patient dialogues.

Machine Learning – Neural Networks and Deep Learning

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Precision medicine, which predicts which treatment regimens might be most effective for a patient based on various patient characteristics and therapy contexts, is one of the most classic machine learning in the healthcare industry. It is a popular application.

The majority of machine learning and precision medicine applications demand a training dataset for which the outcome variable (such as when the disease first appears) is known. This process is known as supervised learning.

A neural network is a more advanced type of machine learning. It has been used in medical research for many years and is a technology that has been around since the 1960s. It is used for classification tasks such as predicting whether a patient will suffer from a certain disease.

It approaches problems in terms of the weights, or “characteristics,” of variables that connect inputs and outputs, as well as inputs, outputs, and all three. Although the connection to the operation of the brain is somewhat tenuous, it has been compared to how neurons interpret signals.

Physical Robot

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Physical robots are already popular. They perform predetermined duties that include lifting, moving, welding, or assembling things in factories and warehouses, as well as transporting equipment in medical facilities.

Robots are now more easily trained to guide them to the desired task and have improved human-robot collaboration. They are also developing greater intelligence due to the integration of additional AI capabilities into their “brains” (really their operating systems).

Surgical robots give doctors “superpowers” by augmenting their vision, allowing them to make precise, minimally invasive incisions, close wounds, and other surgical procedures. Human surgeons still make important decisions.

Rule-Based Expert Systems

The most prevalent AI technology was expert systems, built on a set of “if-then” rules. They have been widely used in the healthcare industry as “clinical decision support” for decades and are still widely used today. Today, most electronic health record (EHR) service providers provide a set of regulations with their systems.

Robotic Process Automation

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This technology performs administratively related structured digital tasks, i.e. information systems as if they were being performed by a human user following instructions or regulations. Compared to other types of AI, they are less expensive, easier to program, and more transparent in their behavior.

Robotic Process Automation (RPA) primarily uses servers with computer applications rather than actual robots. It uses a ‘presentation layer’ interface in which workflows, business rules, and information systems simulate the behavior of a semi-intelligent user.

They are used in the healthcare industry for routine duties such as prior authorization, updating patient records, and billing. They can be used in conjunction with other technologies, such as image recognition, to extract data from fax images and feed it into a transaction system.

We have presented these technologies individually, but they are increasingly being combined and integrated. Robots are getting AI-based “brains” and RPA is integrating image recognition. Perhaps in the future, these technologies will be so interconnected that integrated solutions will be more feasible or practical.
Diagnosis and treatment applications.

Disease diagnosis and treatment have been the focus of artificial intelligence (AI). Such first-principles-based systems showed promise for accurate disease diagnosis and treatment but were not adopted for clinical use. They were not significantly superior to human assessors and had poor integration with clinician and medical record system workflows.

Application programming interfaces (APIs) are a group of “cognitive services”, including data analysis programs based on speech and language, vision, and machine learning.

Patient Engagement and Adherence Applications

Long considered the ultimate hurdle between ineffective and beneficial health outcomes, patient engagement and adherence is the “last mile” issue in healthcare. Utilization, financial results, and member satisfaction all improve as a result of patients taking an active role in their health and care. Big data and AI are being used more and more to address these issues.

Providers and hospitals frequently use their clinical knowledge to create a plan of care that will enhance the health of a patient, whether they are chronic or acute. That often doesn’t matter, though, if the patient doesn’t make the necessary behavioral changes, like losing weight, making an appointment for a follow-up visit, filling medicines, or adhering to a treatment plan. Noncompliance, or when a patient does not adhere to a course of treatment or take the prescribed medications as advised, is a significant issue.

The Future of AI in Healthcare

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Future healthcare options will likely include a significant amount of AI, in our opinion. Precision medicine, which is universally acknowledged to be a much-needed improvement in Healthcare, is primarily enabled by this skill, which takes the shape of machine learning.

We anticipate that AI will eventually become proficient in offering diagnosis and treatment recommendations, notwithstanding the difficulty of the early attempts. Given the quick development of AI for imaging analysis, it appears likely that eventually, a machine will review the majority of radiology and pathology images. Speech and text recognition are already used for activities like patient interaction and clinical note-taking, and their use will rise.

Summary

In addition to administrative procedures within providers, payers, and pharmaceutical organizations, these technologies have the potential to transform many aspects of patient care. Precision medicine, which predicts which treatment regimens might be most effective for a patient based on various patient characteristics and therapy contexts, is one of the most classic machine learning in the healthcare industry.

That often doesn’t matter, though, if the patient doesn’t make the necessary behavioral changes, like losing weight, making an appointment for a follow-up visit, filling medicines, or adhering to a treatment plan.

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