How To Use AI In Healthcare?

AI For Healthcare

Artificial intelligence (AI) technologies, constantly present in modern business and everyday life, are also being steadily applied in healthcare. Artificial intelligence is helpful for healthcare providers in many aspects of patient care and administrative processes, helping them improve existing solutions and overcome challenges faster. Let’s talk about how AI is improving the medical industry in 2022.

What is AI in healthcare?

AI in healthcare is a general term for applying machine learning (ML) algorithms and other cognitive technologies in healthcare settings. In simple terms, AI is when computers and other machines mimic human cognition and can learn, think, make decisions, or take actions. Thus, AI in healthcare uses devices to analyze and process medical data, usually to predict a specific outcome.

An essential detail of the AI use in healthcare is machine learning and other cognitive disciplines for medical diagnostics. AI helps doctors and healthcare providers make more accurate diagnoses and plan treatment using patient and other valuable data. In addition, AI makes healthcare more predictive and proactive by analyzing large amounts of data to develop patient preventive care recommendations.

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Types of AI relevant to healthcare

Artificial intelligence is not one technology but a combination of them. Most of these technologies are directly relevant to the healthcare industry, but the specific processes and tasks they support widely vary. Some specific artificial intelligence technologies of great importance have been identified and described below.

Machine learning

Machine learning is one of healthcare’s most widespread forms of artificial intelligence. It’s a broad approach that underpins many systems to artificial intelligence and healthcare technologies, and many versions exist. When using artificial intelligence in healthcare, precision medicine is the most common application of traditional machine learning. The ability to predict which treatments are likely to be successful for patients based on their composition and treatment design is a huge step forward for many healthcare organizations. Most artificial intelligence technologies in healthcare that use machine learning and precision medicine applications require training data for which the final result is known. It is known as supervised learning.

Natural language processing

Understanding human language has been the goal of artificial intelligence and health technology for over 50 years. Most NLP systems include speech recognition or text analysis followed by translation. Everyday use of AI in healthcare is in NLP applications that understand and categorize clinical documentation. NLP systems analyze unstructured clinical records of patients, giving incredible insight into quality, improved methods, and better outcomes.

Rule-based expert systems

Expert systems based on variations on if-then rules were the dominant artificial intelligence technology in healthcare in the 1980s and beyond. Artificial intelligence in healthcare is widely used to support clinical decision-making. Many of the electronic health record (EHR) systems now provide a set of rules and software.

Expert systems usually involve experts and engineers to create an extensive set of rules in a particular area of ​​knowledge. They function well up to a point and are easy to track and process. But as the number of regulations gets too large, usually more than a few thousand, the rules can start to conflict with each other and fall apart.

Physical robots

By this point, physical robots are well known, given that over 200,000 industrial robots are installed worldwide each year. They perform predetermined tasks such as lifting, moving, welding, or assembling objects in locations such as factories and warehouses and delivering materials to hospitals. Last years, robots have become more collaborative with humans and were easier to train to complete the desired task. They have also become more intelligent as other AI capabilities are built into their «brains» (actually their operating systems).

Robotic process automation

Such technology performs structured digital tasks for administrative purposes, i.e., related to data systems, as if they were a human user following a script or rules. Compared to other types of AI, they are inexpensive, easy to program, and transparent in operating. Robotic process automation (RPA) doesn’t involve robots — just computer software on servers. It relies on the union of workflow, business rules, and «presentation layer» integration with information systems to perform as a semi-intelligent user of the systems.

Why is Artificial intelligence so vital for healthcare?

Healthcare is one of the crucial sectors in the broader extensive data landscape due to its fundamental role in creating a productive and prosperous society. Applying AI to medical data can be a matter of life and death.

Critical stakeholders in the use of AI:

  • Teams of clinicians, investigators, or data managers involved in clinical trials: AI allows them to speed up finding and validating medical coding, which is critical to conduct clinical trials.
  • Health care payers can: personalize their health care plans by connecting a virtual agent via conversational AI to participants interested in personalized health solutions.
  • Clinicians: they improve and customize patient care and review medical data to predict or diagnose disease faster.

Artificial intelligence can predict and track the spread of infectious diseases by analyzing data from government, healthcare, and other sources. As a result, AI could play a critical role in global health as a tool to fight epidemics and pandemics.

Some words about applications for diagnosis and treatment

Diagnosing and treating diseases have been at the heart of AI in healthcare for the past 50 years. Early rule-based systems had the potential for accurate diagnosis and treatment of disease but were not fully accepted in clinical practice. They weren’t significantly better at diagnosing than humans, and integration with physician workflows and medical record systems was far from perfect.

But whether rule-based or algorithmic, using artificial intelligence in healthcare for diagnosis and treatment planning is often difficult to combine with clinical workflows and EHR systems. Integration issues have been a significant barrier to the widespread adoption of AI in healthcare compared to the accuracy of the proposals.

Some EHR software vendors are starting to build limited health analytics AI features into their product offerings but are in the early stages. Providers will have to implement large-scale integration projects to take full advantage of using artificial intelligence in healthcare through an autonomous EHR system.

How To Use AI In Healthcare

All the benefits of administrative applications

There are some administrative applications for artificial intelligence in healthcare. The use of artificial intelligence in hospitals is somewhat less of a game changer in this area compared to patient care. But artificial intelligence in the administrative departments of hospitals can provide significant efficiency. You can use AI in healthcare for various applications, including claims processing, clinical documentation, revenue cycle management, and medical records management.

Another application of artificial intelligence in healthcare applicable to claims and payment administration is machine learning, which allows correlating data from different databases. Insurers and service providers must validate the millions of claims made every day. Identifying and correcting coding problems and incorrect statements saves time, money, and resources.

Best examples of using AI in the healthcare

Whether a hospital or an individual clinic, health care activities are still a complex and multifaceted series of processes. From internal operations such as Human Resources to handling insurance claims and receiving patient data from other providers, data constantly flows in and out of healthcare operations. Decades ago, it was a lot of paperwork and phone calls. Recently, it has been simplified to email and files, and in the past few years, much of the healthcare industry has moved to cloud-based databases and user applications.

  • Supports medical image analysis: AI is used as a case triage tool. It keeps the doctor reviewing images and scans. It allows radiologists or cardiologists to gain critical information to prioritize urgent cases, avoid potential errors when reading Electronic Health Records (EHR) and make more accurate diagnoses.
  • Reducing the cost of drug development: supercomputers help to predict, from databases of molecular structures, which potential drugs will and will not work for various diseases.
  • Unstructured data analysis: clinicians often struggle to keep abreast of the latest medical advances while providing patient-centric quality care due to the sheer volume of medical data and records. Electronic health records and biomedical data collected by medical units and healthcare professionals can be quickly scanned using machine learning technologies to give physicians fast and reliable answers.
  • Prediction of kidney disease: acute kidney injury (AKI) can be challenging to detect, but it can deteriorate patients very quickly and become life-threatening. An estimated 11% of hospital deaths are due to a failure to identify and treat patients, so early prediction and treatment of these cases can go a long way in reducing the duration of treatment and the cost of kidney dialysis.
  • Valuable assistance to emergency medical staff: during a sudden heart attack, the time between calling 911 and the arrival of an ambulance is critical to recovery. Emergency dispatchers must recognize the symptoms of cardiac arrest to take appropriate action to increase the chances of survival. AI can analyze both verbal and non-verbal cues to make a diagnosis from a distance.
  • Research and treatment of cancer, especially in radiotherapy: in some cases, radiotherapy may not have access to a digital database to collect and organize electronic health records, making cancer research and treatment difficult. Oncora Medical has provided a platform that collects relevant patient medical data, assesses the quality of care, optimizes treatment, and offers detailed oncology results, data, and images to help clinicians make informed decisions about radiation therapy.
  • Genetic medicine discovery and development: AI is also being used to rapidly discover and rapidly develop medicines with a high success rate. Genetic diseases are caused by altered molecular phenotypes, such as protein binding. Predicting these changes means predicting the likelihood of genetic diseases. It’s possible by collecting data on all identified compounds and biomarkers relevant to specific clinical trials.

Artificial intelligence and machine learning in the medical industry have brought data management benefits. By applying these tools to real-time data, reports and resource usage metrics can be generated automatically, significantly saving processing time and response time. Predictive modeling at both micro and macro scales also provides a better balance of resource usage and identifies situations and seasons when organizations need to scale.

Main challenges with integrating artificial intelligence into healthcare systems

AI-based medical services have often proved limited in the actual clinical impact that was originally promised. These technologies have several common pitfalls that, if not monitored or corrected in real-time, will continue to degrade their performance in environments outside the lab they were created in.

Challenge 1: integrating AI into Current Tools. Analysts ideally want to incorporate AI capabilities into their basic workflows and business intelligence tools. However, most AI tools are standalone applications requiring specialists to learn and implement another program, language, or integration point. Either approach limits the incorporation of AI into key analytics workflows.

Challenge 2: integrate AI expertise across organizations. AI integration requires data science expertise and leaders trying to fill this knowledge gap face some challenges:

  • Health systems cannot adequately fund data science resources.
  • Even with in-house talent in data science, scaling that expertise to larger groups of analysts is difficult.
  • Teaching existing teams data science concepts and skills is a slow, expensive, and unsustainable process.

These difficulties slow down the pace of AI implementation in various areas of medical institutions.

Challenge 3: show the positive impact of AI. When healthcare institutions succeed in implementing predictive models, they struggle to explain the effect and inspire trust across the organization. Some of the more common problems include:

  • Limitations of black box software: most traditional AI solutions are black boxes, making it impossible to validate, modify, or optimize the analysis. Managers are left wondering if these programs work and how to improve them.
  • The narrow focus on predictive analytics. Many of these solutions focus on predictive analytics, limiting any potential impact. While predictive modeling is a critical use case, AI support extends across the enterprise, including critical decision-making.
  • There is a risk of bias when utilizing AI in healthcare if the data used to teach algorithms is not representative of the general population.

Finally, we can see a lack of standardization across various AI systems, making it difficult to compare results or combine insights from multiple sources. These problems will be overcome eventually, but it will take much longer than it takes for the technologies themselves to mature. As a result, we will see limited use of AI in clinical practice within five years and more widespread use within ten years.

The Future of AI in healthcare

Medical experts are confident that AI will play an essential role in the healthcare offerings of the future. Machine learning is the primary capability behind the development of precision medicine, which by all accounts is a much-needed step forward in treatment. Although early attempts to guide diagnosis and treatment proved challenging, AI is expected to master this area as well, eventually. Given the rapid development of AI in image analysis, it seems likely that most radiology and pathology images will be machine-inspected at some point. Speech and text recognition allows for solving problems such as communicating with patients and recording clinical notes, and their use will grow.

AI systems must be approved by regulators, integrated with electronic health record systems, standardized enough that similar products work similarly, trained by physicians, paid for by public or private payers, and updated over time to be widely adopted.

Final words

The implementation of artificial intelligence in healthcare still faces challenges, such as distrust of the results provided by the machine learning system and the need to fulfill specific requirements. However, the use of AI in healthcare has already brought many benefits to healthcare stakeholders.

Helping medical and non-medical staff perform repetitive tasks, assisting users in finding answers to queries faster, and developing innovative treatments and therapies, patients, payers, researchers, and clinicians benefit from using AI in healthcare by improving workflows and operations.