Artificial Intelligence in Healthcare: Emerging Trends in 2023

Artificial Intelligence is already improving healthcare practices and has the potential to do even more.

Behind every drastic change in any industry, there is always the impact of a catastrophic global crisis. However, unlike other industries, the healthcare industry had been slow to adopt AI innovations before COVID-19 swept the world. This incident has made the healthcare industry face huge challenges compared to other industries, thus giving a boost to healthcare app development initiatives. Among them, artificial intelligence is the focus, and it still is!

In fact, according to data from Prior Research, by 2030, the global artificial intelligence medical market size is expected to exceed US$187.95 billion at the exchange rate in 2022-2030 During the forecast period, the compound annual growth rate is 37%.

In addition, the application of artificial intelligence in healthcare will not be limited to process automation or data science.

Artificial Intelligence in Healthcare Trends in 2023

1. Artificial Intelligence RPA solutions to achieve low-cost, error-free treatment

Optimization of artificial intelligence automated medical processes The expectation is slowly taking the form of real-life applications, not just watching science fiction movies and thinking about the possibilities...

In fact, in many well-equipped hospitals, this kind of robotic process automation has been utilized by medical practitioners in a variety of ways. The best part – Automation of medical processes benefits healthcare providers and patients in getting cost-effective, error-free treatment.

Gartner says that by the end of 2023, about half of U.S. healthcare providers plan to deploy RPA in their medical facilities. In addition, RPA in the healthcare market size is expected to grow to US$6.2 billion by 2030, with a compound annual growth rate of 26.01%, and will be approximately US$2.9 billion in 2022.

Ways to leverage RPA to enhance healthcare operations:

• Apply precise process data to improve productivity of hospital management tasks and patient policy issuance processes;
• In real time Simplify structured and unstructured data record management to facilitate revenue cycle, insurance claims processing and more;
• Adapt to changes in the healthcare industry to transform traditional healthcare operations and reinvigorate the healthcare experience;
• Implement infection control plans, conduct triage management; comply with regulations, conduct patient screening and tracking; inventory management, remind staff to pay attention to sudden peaks, etc.

2. Emotional AI for Mental Health Disorders and Autism

As the world continues to grapple with the spread of the coronavirus, virtual healthcare is one way to avoid further spread. Good choice. Implementing emotional AI in telemedicine applications can provide a heightened experience that is more meaningful and engaging for patients, especially those with mental disorders and autism.

• The use of emotional AI in online consultation APPs can better help doctors interpret, monitor and interpret patients' emotions;
• Not only that, medical applications based on emotional AI can also Use speech analysis to diagnose various mental illnesses, such as depression, dementia, Down syndrome, autism, etc.;
• In addition, it can also understand the emotions of pregnant women and the elderly;
• Remind patients to take their medications on time and continuously monitor their health status.

3. Data-driven personalized healthcare services attract much attention

Many healthcare IT solution providers believe that by 2023, the adoption of personalized healthcare treatments will get promoted.

According to a survey based on healthcare data generation, approximately 80Mb of imaging and EMR data are generated annually on average, and its compound annual growth rate is expected to reach 36% by 2025. This data can be used to gain useful insights for personalization.

In addition, these data can also be generated through wearable devices, such as wristbands, smart jackets (Levi's Google Jacquard), tension bands (Samsung), sensor insoles (Feetme), etc.

Wearable device users can get data in these categories, including steps, heart rate, blood pressure, calories burned, etc. When all this data is collected in AI fitness apps, these apps analyze it, and provide personalized diet and exercise plans.

4. Revolutionizing drug discovery

Similarly, in other healthcare processes, artificial intelligence can also help accelerate the drug discovery and development process, as well as the analysis of results of effective drug combinations.

In 2023, pharmaceutical experts expect greater adoption of artificial intelligence in this specific drug development sector. In fact, according to MarketsAndMarkets, artificial intelligence in drug research and development is expected to exceed $4 billion by 2027, with a compound annual growth rate of 45.7%.

Four major benefits of the application of artificial intelligence in drug research and development:

• Contact with modern biology
• Improved modern chemistry
• Higher The Success Rate
• Cost-Effective Process

The AI ​​trend in healthcare is still growing, with many limitations and challenges that need to be addressed.

5. Ambient Intelligence

Ambient intelligence (AmI) has long been a trend in healthcare solutions, with its unique cross-disciplinary proposal to embed sensors and processors into smart devices , to adjust to human needs.

AmI is committed to the intersection of emerging technologies, including artificial intelligence, Internet of Things, big data, etc.

Therefore, ambient intelligence in healthcare can be leveraged in the following ways:

• By implementing AmIi-driven solutions, reducing the wait for patients to consult a doctor by automating preliminary patient testing Time;
• Automated urgent care support;
• Automatic monitoring of patient vital signs;
• Ambient Assisted Living (AAL) technology solutions.

6. Smart pills eliminate invasive operations

Smart pills are like miniature electronic devices, designed with the appearance of any other ordinary drug capsule, and integrated with cloud computing and wireless communication platforms, Highly advanced clinical operations can be performed, such as sensing with biosensors, imaging, and drug delivery via pH or chemical sensors. Experts also call these pills ingestible sensors, but this is different from wearable and embedded sensors.

Patients can easily take a smart pill that travels throughout the gastrointestinal tract to gain access to hard-to-reach information. Once it serves its purpose, it can be easily removed from the system.

In addition, smart pills can also be used to perform the following medical treatments:

• Diagnostic imaging
• Vital signs monitoring
• Directed drug delivery

7. Chronic disease diagnosis

According to the latest survey by WHO, about 17 million people (under 70 years old) die from chronic diseases every year. The United Nations also released a report on the matter, stating that the number of global deaths due to chronic diseases is expected to rise to 70%.

However, artificial intelligence has brought a glimmer of hope to the diagnosis of chronic diseases and improved the accuracy of treatment by leveraging years of diagnostic data to gain insights into the country. Let’s take a look at how artificial intelligence applications can help treat chronic diseases:

Artificial intelligence treats heart disease

• Artificial intelligence whole-heart computing model provides personalized medicine and understands ventricular rhythm Different scenarios of aberrations;
• Provides predictive analytics with patient-specific models to better assist in cardiac surgery;
• Reduce diagnostic time by examining and analyzing CT scans with data-driven models, and controlling the consequences of brain damage;
• Applying artificial intelligence to ECGs for low-cost testing, detecting weak heart pumps, and predicting heart failure rates.

Artificial Intelligence Detection and Diagnosis of Cancer

• AI/ML model analyzes tissue scans to accurately detect and treat colorectal cancer;
• Machine learning algorithm monitoring Patient response to anti-cancer drugs;
• Predictive machine learning model that can diagnose 15 different types of cancer with an accuracy of 91%.

Artificial Intelligence for Diabetes Care

In 2019, Rensselaer Polytechnic Institute researchers cycled through clinical models based on artificial intelligence and big data analytics to examine through continuous blood glucose observations blood sugar levels and get early warning signs when high risk is detected, which further helps in quick and early diagnosis of diabetes.

In summary, the concept of artificial intelligence in healthcare opens many doors to improve healthcare methods and raises our hopes to the next level for efficient treatments. Moreover, accuracy will improve over time. So, let’s continue to explore the possibilities of artificial intelligence in healthcare and see where it leads the field.

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