Last Updated | December 14, 2021
Advanced technology has already taken the healthcare industry by storm. Besides HL7 integration services, EHR software development, and mobile health app development, Artificial Intelligence (AI) is significantly becoming ever-present in healthcare. While EHR software stores the complete historical medical records of patients, HL7 integration with the help of successful-approaches-for-hl7-integration-implementation simplifies sharing of medical records between healthcare providers.
Amidst this competitive market, AI has proved the potential to assist healthcare practices in many aspects of their administrative processes and patient care delivery. It facilitates patients providing telehealth software as a service which may, however, increase telemedicine startup costs for hospitals and other organizations. Still, the benefits it gives are worth the costs.
The most common form of artificial intelligence used in healthcare is machine learning. ML helps healthcare organizations make predictions about what treatment will possibly be successful with patients and develop personalized medications for them.
The use of AI benefits patients, doctors, administrators, and practices by simplifying many complex health-related tasks that typically require human intelligence. AI handles those tasks now at a faster rate.
How Is Artificial Intelligence Used In Healthcare?
The use of artificial intelligence in healthcare can be described as the application of machine learning algorithms and software to imitate human cognition in the evaluation, presentation, and comprehension of complex medical data.
From discovering links between genetic codes to driving surgery, artificial intelligence technology has been a boon to the healthcare sector.It aims to develop machines that can predict, discern, learn, and act at a faster rate than human beings to avoid some deadly human errors caused by incomplete patient histories and errors in diagnosing diseases in medical records. AI analyzes data throughout the healthcare systems to maximize the efficiency of healthcare practices for disease diagnosis and prevention.
In addition to these practices, AI programs are also applied to treatment protocol development, drug development, personalized medication, and patient monitoring.
The primary purpose of the application of AI in healthcare is to examine if the prevention or treatment techniques can bring the expected patient outcomes.
What Are the Main Types Of AI And Its Applications In Healthcare?
Being one of the most popular types of AI, machine learning has virtually numerous applications in healthcare. It aims to modernize workflows, facilitate access to clinical data, and improve the accuracy and flow of medical information.
ML provides imaging diagnostic ability to make quick and precise diagnoses, as well as identify which patients can benefit from new therapies and treatments. Moreover, radiologists also use ML in the form of 3D radiological images to distinguish between healthy anatomy and tumours.
Natural Language Processing
Natural language processing (NLP), another type of AI, provides shape to the unstructured data of the healthcare universe. It gives insights into understanding care quality, improving methods, and developing better results for patient care. Without NLP, the data is complex and not in the right format for modern computer algorithms to extract insights.
So, NLP can scrub unstructured data to identify previous improperly coded patient cases and discover the diseases.
Rule-Based Expert Systems
Rule-based expert systems are computer systems that are designed to imitate the diagnostic decision-making faculty of human experts.
Moreover, with the help of telemonitoring data, rule-based expert systems can create automated alerts and generate patient instructions to improve clinical management. These systems are developed to diagnose Cholera, Malaria, Tuberculosis, Breast Cancer, and Typhoid Fever.
Some noteworthy examples of this type of system are DXplain for general medicine and Mycin for contagious diseases.
Virtual Nursing Assistants
Virtual nursing assistants are applications designed to support populations with common medical conditions as well as track a patient’s blood pressure, weight, and other parameters.
These apps utilize AI analytics to monitor patients’ medication intake, other health parameters, and habits to avoid worsening physical conditions. Being multi-functional nurses, they incorporate chatbots to allow patients to discuss their health-related problems with their physicians, handle their requests privately, and conveniently book appointments. A virtual nursing assistant is a great instance of AI that remains available 24/7 and provides quick answers to patient queries.
Benefits Of Artificial Intelligence In Healthcare
The application of AI technology in healthcare has numerous advantages for hospitals, practices, and other large-scale medical organizations. Human brains developing this technology aspire to make the most of machines for automating healthcare tasks and saving more time for doctors to expand the delivery of care to as many people as possible. So, here we have listed some of the notable benefits of AI in healthcare:
It is robot-assisted surgery that makes surgical operations more quick and accurate. Many individuals fear the use of robots in healthcare for surgery since they think AI-powered robots are decision-makers about surgical flow. However, the process stays in the control of a human, and robotic technology only assists surgeons with delicate and more precise surgical motions.
For example, Mayo Clinic in Florida uses AI-powered robots to help with the execution of abdominal surgeries and now developing more for operating on the brain.
Many states provide their population with healthcare claims to help them receive medical services even when they cannot afford the expensive costs. However, the healthcare claims can be fraudulent that means hundreds of dollars can be lost annually. AI solves this problem as well. When AI is around, fraud can be avoided with its automated claim assessment. AI-supported machine learning models detect invalid claims and increase the pace of processing, approving, and paying the valid ones.
Besides these insurance claims, AI assists with other fraud detections as well. It can find out the billing patients never received and prevent patient data from being stolen.
Prescription Error Recognition
Errors in prescriptions cause 5000 to 7000 deaths annually in the United States alone.
The errors often stem from faulty EHR interfaces that confuse doctors who choose the wrong drugs from drop-down menus or make errors while dosing units. However, with artificial intelligence, ML models can perform analysis of EHR data and compare every patient’s new prescriptions against it. The prescriptions that do not meet the usual patterns get flagged, thereupon doctors can review and rectify them.
Take the example of Brigham and Women’s Hospital that uses AI-powered software to locate prescriptions errors and get them fixed.
Future Of AI In Healthcare
Artificial intelligence is all set to be the next game-changer in the healthcare industry. By helping with drug discovery and forecasting kidney disease, AI is reshaping the medical industry and becoming more common in various healthcare fields and specialities. With the growing needs of the population, artificial intelligence is also growing to offer innovative solutions for diagnosing conditions, developing treatment plans, monitoring health epidemics, identifying diseases, and creating efficiencies in medical workflows.
By incorporating machine learning, deep learning, and natural language processing, AI is enabling medical professionals and healthcare stakeholders to figure out medical needs and solutions faster and accurately. It works by using data sets to make well-informed medical decisions quickly.
AI has already taken over every task- ranging from answering phone calls, reviewing medical records and therapeutic device and drug design to making a clinical diagnosis and developing a treatment plan, and even communicating with patients.
It is conceivable to analyze the future of technology that will transform the healthcare industry by automating every healthcare process and producing more technologies to speed up the process of delivering quality care.
What Are The Challenges Of AI In Healthcare?
There is no doubt that the role of artificial intelligence in healthcare is vital. It provides many facilities, making patient care delivery and patient experience better than ever. However, the use of artificially intelligent systems has its own limitations and challenges in the healthcare sector. So, here are some of the challenges listed below:
Availability of a massive amount of data
The first step towards developing an ML system is data collection. The well-performing models can only be developed with a massive amount of high-quality data. However, the data collection process becomes controversial due to the privacy concern of patients’ health information that they may not be willing to reveal. So, it leaves professionals with a restricted amount of data that they can use for training models. Since this limited data is insufficient to train models well, the developed models will have less potential to diagnose diseases accurately.
Creating Biased Data Models
Due to privacy concerns and data breaches, models are developed with only a portion of collected data. It means the data used for training models is biased. So, it will target a particular race, gender, and specific age group. However, this should not be the case. The collected data must be a true representation of the population for which it is intended to be used.
Possibility of Errors
Since AI systems are machines, they are likely to make errors. They can give wrong results that may cause risks to a patient’s health and result in other health-related problems.
What Are Examples Of Artificial Intelligence In Healthcare?
Examples of artificial intelligence in healthcare include medical imaging analysis, drug discovery, forecasting kidney disease, researching and treating cancer, and a lot more. These AI systems aim to assist medical professionals and patients in the care process, making it easy and efficient. So, let’s explore these examples more:
Medical Imaging Analysis
Being used as a case triage tool, AI supports professionals in reviewing scans and images. The instances of medical imaging are screening for detecting common cancers and identifying cardiovascular abnormalities. It enables cardiologists and radiologists to prioritize critical cases by identifying essential insights in the medical records of patients.
AI algorithms can determine new drug application, their potential toxicity, and mechanisms of action. They have led the top digital health companies to develop a drug discovery platform that enables drug manufacturers to repurpose bioactive compounds and existing drugs.
This advanced ML tool is used to develop insights from complex biological datasets reducing the risk of human errors and bias.
Forecasting Kidney Disease
AKI (Acute Kidney Injury) can cause fast deterioration to patients and become life-threatening for them. However, it can be difficult for clinicians to detect the disease. So, to reduce the threat to the lives of patients, companies developed an ML tool that can predict the risk of the disease and help with early treatment of it. Consequently, it reduces the life-long treatments and the costs of kidney dialysis.
Besides these AI examples, Epic integration is also a good example that incorporates AI capabilities like augmenting decision-making with embedded machine learning algorithms to enhance care delivery.
Many hospitals across the globe, including some of the top US-based hospitals such as Mayo Clinic, Rochester, and Cleveland Clinic, Ohio are using Epic’s AI capabilities.
What are the uses of robotics in healthcare?
Robotics in healthcare helps medical providers by assisting in surgery, streamlining practice logistics, and freeing up more time for them to shift attention to patients. Examples of robotics used in healthcare include telemedical networks to expand accessibility, the use of powerful exoskeletons to improve mobility, and metalheads for surgical precision.
Examples of artificial intelligence in medical imaging diagnostics
The use of AI in medical imaging diagnostics is evolving extensively. Examples of artificial intelligence in healthcare for imaging diagnostics include the following:
- Identification of cardiovascular abnormalities
- Screening for detecting common cancers
- Detections of fracture, dislocations, and other musculoskeletal injuries
- Diagnosing neurological disease
Which is the best application of AI in the healthcare sector?
The best application of AI in healthcare is the accurate detection of illnesses, resulting in the reduction of errors. Besides providing this capability, AI assists with facilitating healthcare asset management software to help hospitals track equipment, machinery, and other physical assets to ensure better healthcare delivery.
HIPAA law requires healthcare organizations to ensure physical security, administrative security, and technical security of medical records to keep patient information secure.
AI-powered technology is transforming healthcare by introducing more robotic technology and virtual assistance that enhances care delivery efficiency. It helps the doctors identify the contagious diseases early and instantly develop the right treatment plans for patients.
Therefore, AI is a great support to the endeavours of healthcare professionals to save more lives with precise treatments and efficient care delivery.