Artificial intelligence is already improving healthcare approaches and has the potential to do much more.
Behind every drastic revolutionization in any industry, there’s always the impact of any disastrous global crisis. In addition, unlike other industries, the healthcare sector was taking the turtle steps towards adopting AI innovations until COVID-19 hit the world. And this event made the healthcare sector deal with ample challenges as compared to other sectors, which resulted in the boost of healthcare app development initiatives, in which AI was the buzzword; and still is!
In fact, as per the Precedence Research, the global artificial intelligence in healthcare market size is projected to cross the USD 187.95 billion exchange rate by 2030 at a CAGR of 37% during the forecast period of 2022-2030.
Further, the adoption of AI in healthcare isn’t only limited to process automation or data science, but beyond that, which we are going to reference throughout this blog.
Excited? We also can’t wait to delve into the world of AI trends for the healthcare industry to follow in 2023 and beyond. So, let’s get it started:
Yes, that expectation from AI to automate healthcare processes is slowly taking the shape of real-life applications rather than just watching sci-fi movies and thinking about possibilities, “What if…!”
In fact, in many high-facility hospitals, this Robotics Process Automation has been utilized by medical practitioners in many ways. The best part – healthcare process automation is beneficial for both healthcare service providers and patients to get error-less treatments with cost-effectiveness.
By the end of 2023, around half of the US-based healthcare providers are planning to deploy RPA in their healthcare facilities, says Gartner. In addition, the RPA in the healthcare market size is expected to see a rise to USD 6.2 billion by 2030 at a CAGR of 26.01%, which was around USD 2.9 billion in 2022.
Ways RPA can be leveraged to enhance the following healthcare operations:
- Apply precise process data to boost the productivity of hospital administrative tasks and patient policy issuance processes;
- Streamline the structured and unstructured data record management in real-time to boost the revenue cycle, insurance claim processing, etc.;
- Adapt to healthcare industry change to transform legacy healthcare operations and revitalize the healthcare experience;
- Execute infection control protocol with triage management, patient screening tracking with regulatory compliance, inventory management, alerting staff about the sudden rush, etc.
Today, when the world is still dealing with the Coronavirus spread situation, a virtual healthcare facility seems like a great option to avoid further spread. Speaking of which, an implementation of Emotion AI in telemedicine apps can offer an elevated experience, which is more meaningful and engaging for patients, especially the ones with mental disorders and autism.
- Emotion AI used in online doctor consultation apps can better help doctors to read, monitor, and interpret the emotion of patients;
- Not just that, emotion AI-powered healthcare apps can also utilize voice analysis to diagnose various mental disorders, like depression, dementia, down syndrome, autism, etc.;
- Apart from such, to understand the emotions of pregnant women and elders;
- Moreover, reminding patients about taking medication on time and continuously monitoring their well-being.
Many healthcare IT solution providers believe that the year 2023 will be all about getting an elevated adoption drive for personalized healthcare treatments.
As per the healthcare data generation-based survey, on average, around 80 Mb of imaging and EMR data is being generated each year, and its compound annual growth rate is expected to reach 36% by 2025. Additionally, this data can be used to get useful insights to avail personalization.
Moreover, this data can also be generated using wearable devices, such as a wristband, smart jacket (Levi’s Google Jacquard), Tension Belt (Samsung), Sensorized Insoles (Feetme), and many others.
Wearable users can get data in the categories, including steps, heart rate, blood pressure, calories burnt, etc., and when all this data is gathered in AI-powered fitness apps, which perform analysis and offer personalized diet and exercise plans.
Similarly, with other healthcare processes, AI can also help to accelerate the processes of drug discovery and development along with result analysis for the effective drug combination.
In the year 2023, pharmaceutical experts are expecting to get more adoption of AI for this specific drug discovery department. In fact, as per the MarketsAndMarkets survey, AI in drug discovery is projected to surpass USD 4.0 billion by 2027 at a CAGR of 45.7%.
AI in drug discovery unlocks doorways to four benefits, which are as follows:
- Access to modern biology
- Improved, modern chemistry
- Higher success ratios
- Cost-effective processes
This AI trend in healthcare is still under enhancement, which has many limitations and challenges to solve.
For over a long time, Ambient Intelligence (AmI) has been in healthcare solution development trends with its multi-disciplinary, unique proposal to sensors and processors embedded into smart devices to adapt according to humans’ needs; and that too, seamlessly.
Yes, you’ve guessed it right! AmI works at the intersection of emerging technologies, which include Artificial Intelligence, IoT, Big Data, and many others.
So, Ambient intelligence in healthcare can be utilized in the following ways:
- Cutting out patients’ waiting time to consult a doctor through automated patient preliminary tests by conducting AmI-powered solutions;
- Automating emergency care support;
- Automating monitoring of patient’s vital;
- Ambient Assisted Living (AAL) technology solution.
Smart Pills are like tiny-shaped electronic devices designed with the look of any other normal pharmaceutical capsules and cloud computing and wireless communication platform integrations, doing highly advanced clinical operations, such as sensing using biosensors, imaging, and drug delivery through pH or chemical sensors. Experts also call these pills ingestible sensors, which separate them from wearable and embedded sensors.
Patients can easily consume a smart pill, which spans its traversal to the gastrointestinal tract to get difficult-to-obtain information. Once its purpose is fulfilled, it can easily be removed from the system.
Apart from these, smart pills can be used to conduct the following medical procedures:
- Diagnostic Imaging
- Vital Sign Monitoring
- Targeted Drug Delivery
As per the latest WHO survey, around 17 million people (<70 years of age) die from a chronic disease every year. Moreover, The United Nations also circulated a report on this matter, which claims that the global deaths due to chronic diseases are expected to rise to 70%.
However, AI brings a ray of hope for chronic disease diagnosis with improved accuracy in treatment by utilizing years of diagnosis data insights. Now, let’s know ways AI applications can help in treating the most concerning chronic diseases:
- AI-powered whole-heart computational models provide personalized medicine to understand different conditions of ventricular arrhythmias;
- Patient-specific models provide predictive analytics to better get help in cardiac procedures;
- CT scans are examined and analyzed by data-driven models to reduce diagnosis time and control the brain damage consequences;
- Applying AI to ECG for low-cost tests, detecting weak heart pumps, and predicting heart failure rates.
AI to Detect and Diagnose Cancer
- AI/ML models to analyze tissue scans to accurately detect and treat colorectal cancer;
- Machine learning algorithms to monitor patients’ response toward cancer-fighting drugs;
- John F. McDonald and his team have worked on predictive machine learning models to diagnose 15 different types of cancers using 499 independent data sources. Further, these models have delivered results with 91% accuracy.
AI for Diabetes Care
In 2019, Rensselaer Polytechnic Institute researchers circulated research on AI and Big data analytics-based clinical models to check blood sugar levels with continuous glucose observation and get an early warning sign when the high-risk is detected, which further helps in quick and early diabetes diagnosis.
Indeed, the AI concept in healthcare has unlocked many doors to improve healthcare approaches and taken our hopes to the next level to get highly effective treatments. Of course, the accuracy levels will increase with time. So, let’s keep exploring possibilities for AI in healthcare and see where it leads this sector.