Artificial intelligence continues to play an essential role in our day-to-day lives. The emergence of artificial intelligence in healthcare promises to improve medical outcomes going into the future.
In the healthcare industry, the use of AI-based technology has been accelerated by the Coronavirus pandemic and is expected to increase the AI healthcare market size from $10.4 billion in 2021 to $120.2 billion in 2028. Additional factors contributing to projected market growth include:
- Decreased healthcare spending
- Rising demands of personalized medicine
- The inflow of patient health-related digital data
While applications of artificial intelligence spread across many industries like transportation, entertainment, manufacturing, education, human resources, and more, today’s article explains how is AI used in healthcare, the benefits of AI in medical field wearable technologies, and the future of artificial intelligence in healthcare wearable technologies.
Applications of AI in Medical Field Technology
Artificial intelligence (AI) advancements have been unprecedented, outperforming the development speed principle of Moore’s Law while opening the door to wearable healthcare technologies like fitness trackers, biosensors, smart health watches, and heart rate monitors.
While AI used in healthcare has vastly improved patient care from robotic surgeries, workflow efficiencies, and clinical treatment plans, there’s been a large shift in the commercialization of wearable healthcare technology made notable by leading fitness and technology providers like FitBit, Nike, and Apple.
Providing real-time continuous monitoring of human physiology, among the type of data collected by wearable healthcare devices include:
- Heart rate
- Active minutes
- Blood pressure
- Calories burned
- Sleeping patterns
- Distance traveled
- Irregular heart rhythms
- Blood oxygen levels (SpO2)
As a whole, the data collected from healthcare wearables can be used for wellness tracking, clinical research, and real-time patient monitoring. Given the large volume and complexity of data being collected, machine learning is often used by organizations to streamline data-driven decisions.
Uses of Machine Learning in Medicine
Machine learning (ML) is a rapidly growing branch of AI that uses a machine-learning algorithm to automate analytical model building, learn from data, and identify patterns to make decisions with minimum human intervention.
Today, ML techniques are being used to predict health conditions such as:
- Liver disease
- Breast cancer
- Heart disease
- Thyroid cancer
- Kidney disease
Additional applications of machine learning in healthcare include:
Medical Imaging and Diagnostics
Healthcare providers have long been reliant on medical imagery technology and real-time monitoring systems to improve patient care. In hospital settings, we depend on a subset of machine learning known as deep learning to improve image analysis and patient monitoring.
These include smart healthcare system and services such as:
- Gait analysis
- Neonatal monitoring
- Nurse calling system
- Medication monitoring system
- Periodic clinical reassessment
- Sleep detection and monitoring
- Physiological parameter monitoring
- Tracking Leukemia remission rates
- Patient alert system for fall monitoring
Given the advancements of AI technology in medical field practices, we’ve seen a crossover of smart healthcare systems into commercial healthcare wearable technologies. Two such examples include wearable devices that help those who are visually impaired, deaf, or blind:
- Multifunctional AI hearing aids that help deaf people “feel” their environment and is capable of using natural language processing
- Sonar and echolocation AI healthcare wearables that perform haptic vibrations to inform users how close (or far away) objects are from them
One of the leading benefits of artificial intelligence in healthcare includes clinical drug development and it primarily contributes to how artificial intelligence is used in healthcare.
By using machine learning approaches to handle big data sets from publications, in-house experiments, or data repositories, healthcare providers can make more data-driven decisions. Some ways how machine learning approaches can be applied during early discovery include:
- Predicting target structures
- Identifying and optimizing “hits”
- Exploring and marking the biological activity of new ligands
- Designing models predicting pharmacokinetic and toxicological properties of drug applicant
Natural Language Processing (NLP)
Natural language processing is another subset of artificial intelligence which enables computers to understand, interpret, manipulate, and respond to text or voice data as humans do.
Examples of AI in healthcare concerning NLP include:
- Augmenting hospital triage systems
- Detecting early-stage chronic diseases
- Aiding in the prediction of patient outcomes
Without machine learning models or NLP technology, healthcare data would not be extractable with modern computer-based algorithms. With NLP technology, however, healthcare providers are able to:
- Enhance clinical decisions
- Accelerate clinical trial matching
- Develop clinical practice guidelines
- Improve health record documentation
|For more information, visit our blog on Addressing the Data Imbalance Problem in Healthcare.|
Benefits of AI in Healthcare Wearable Technologies
Rapid advancements in artificial technology in healthcare and patient care have paved the way for many of the benefits we find available in healthcare wearable technologies today. These include additional advantages, such as:
- Sleep monitoring
- Active fall detection
- Prediction of diabetes
- Around-the-clock health monitoring
- Improving chronic disease prediction
- Enhanced telehealth solutions with 24/7 patient monitoring
Future Outlook of ML and Artificial Intelligence in Healthcare
Now that we’ve answered how is artificial intelligence used in healthcare wearables, let’s take a look at additional examples of artificial intelligence in healthcare:
- Cybersecurity – Artificial intelligence is already being relied on to protect healthcare organizations from automating phishing attacks, strengthing existing security protocols and infrastructure, and closing regulatory compliance gaps.
- AI-Assisted Surgery – Advancements such as automated suturing, surgical workflow modeling, surgical skill evaluation, and post-surgery remediation as already being used.
- Patient Care Administrative Tasks – Artificial intelligence in the healthcare industry can streamline administrative applications such as claims processing, revenue cycle management, medical records management, and clinical documentation.
- Clinical Trials Diagnosis and Treatment – This technology is already being utilized in data-intensive specialties like ophthalmology, pathology, and radiology and will likely cross over other medical fields to improve clinical decision support.
- Improve Chronic Patients Conditions – AI-powered algorithms can analyze large amounts of data for patients, swiftly identify health deterioration, and quickly alert doctors about severe cases.
The Biggest Challenges of AI in Healthcare
As with all industries undergoing innovation, there are challenges and disadvantages of artificial intelligence in healthcare that must be faced. Among the biggest challenges include:
- Complexity – Creating intuitive AI and ML software and systems requires extensive knowledge often only found amongst leading healthcare technology providers.
- Interpolation – There are many incompatibilities between healthcare data systems and legacy systems where optimizing ePHI and record practices could improve personal treatment solutions.
- Data Governance – Electronically protected health information is personal and requires consent.
- Transparent Algorithms – There is a demand for ensuring algorithms are transparent and understood, meet strict drug regulations, and have no bias.
- Intellectual Property Laws – Defining AI and big data is a risky venture that dives into data privacy, data protection and privacy (regulatory compliance), and ethical safety.
|For more relevant information, visit our articles on:
Create Custom ML and AI Healthcare Wearables
Much of how AI is used in healthcare has changed across hospital settings, patient care, and day-to-day life.
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