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Role of AI in The Healthcare Sector

Published: 06 Apr 2021

Role of AI in The Healthcare Sector

1.  AI – Its Significance in Healthcare
2.  Benefits of using AI in the Healthcare Domain
3.  Some of the prominent AI Healthcare applications
4.  9 Significant use cases of AI in healthcare”
5.  Conclusion

AI – Its Significance in Healthcare

Artificial Intelligence (AI), Machine Learning (ML), and its related technologies are today used in various business applications across industries of healthcare, pharma, automobile, banking, finance, insurance, etc. These AI applications not only deliver great results for businesses by simplifying business processes but also improves the human experience as a whole. Specifically, though AI is being leveraged across many industries, its dominance is more prominent in the healthcare sector.

Since the pandemic, the healthcare sector has been expanding in leaps and bounds with more demand for quality healthcare. Undoubtedly, though AI is touching every sphere of human life, the way it is transforming healthcare is truly phenomenal. Analysts believe that AI is poised to become a transformational force in the coming years for the healthcare domain.

AI testing

According to MarketsAndMarkets, “The global AI in healthcare market size is expected to grow from USD 4.9B in 2020 and reach USD 45.2B by 2026 and is projected to grow at a CAGR of 44.9% during the forecast period.” The major factors driving the AI in healthcare market growth are the increasing volume of healthcare data, growing complexities of datasets, the intensifying need to reduce towering healthcare costs, the eventual need to deliver better patient outcomes, etc.

According to reports by Frost & Sullivan, the healthcare AI market is likely to experience a CAGR of 40% by 2021, and it has the potential to change healthcare outcomes by 30-40% and cut treatment costs in half.

As outlined by many leading research analysts, there has been more complexity and rise of data in healthcare which essentially demands the use of AI within this field. There are various ways in which AI is being employed by different healthcare providers and various life science companies which have been cited vividly in an article on the topic ‘Potential for Artificial Intelligence in Healthcare’ published by Future Healthcare Journal. Many of these publications state that the usage of AI in healthcare has been increasing in recent times and also state many advantages of leveraging AI in healthcare.

Benefits of using AI in the Healthcare Domain

benefits of AI in healthcare

1. Better patient outcomes:

AI in healthcare

Leveraging AI helps to bring-in better treatment methods and helps improve patient outcomes and their survival rates. There are various AI applications used to detect and analyze huge volumes of data and make effective predictions through ML algorithms. These predictions continue to better the patient health predictions with the AI’s new computing power. Typically, all these predictions have been helping healthcare providers to deliver better patient outcomes.

2. Simplify healthcare with better data-driven decisions:

Artificial intelligence in healthcare

ML algorithms have the potential to provide data-driven clinical support decisions to both the doctors, physicians, and also to hospital staff. Evidently, this continues to simplify healthcare as AI uses algorithms and data to give automated insights that can be best used by healthcare providers to derive real-time decisions.

3. Reduce healthcare costs:

AI in Healthcare - reduce costs

It has been stated that AI in healthcare helps identify inefficiencies within the systems and streamlines administrative processes and ensures healthcare cost reduction.

4. Screen diagnostic reports on par with radiologists:

Screen diagnostic reports - AI

Some of the AI imaging tools can be used to screen chest x-rays for signs of tuberculosis and they provide more accurate readings. Moreover, in places with low resource areas, many AI tools can be deployed to reduce the need for trained diagnostic radiologists and can be used to screen patient radiology reports.

5. Deliver customized treatment plans:

AI healthcare - customized treatment plans

By leveraging AI & ML algorithms, doctors can deliver customized treatment plans after a thorough study of the AI patterns being produced. These patterns help deliver customized treatments based on analyzing various patterns of similar patient’s data

6. Reduce mortality rate:

Reduce mortality rate - AI in healthcare

There are certain AI applications being used by healthcare providers that have real-world use cases. These real-world use cases greatly help to make the diagnosis, treatment, and monitoring of patients more accurate, reliable, and also help reduce the patient mortality rates in certain cases

7. Enhance primary care and triage with chatbots:

AI in Healthcare - enhance primary care

Patients can use AI-powered chatbots to avoid trips to their doctor, as these bots provide instant answers to all patient health-related queries. These bots guide them with minimal medical instructions. These chatbots are available 24×7 and can deal with multiple patients at the same time

8. Reduce re-admission rates drastically:

AI testing

AI tools are being leveraged to monitor respiratory rate, oxygen levels, pulse, blood pressure, and body temperature of patients. Eventually, it is helping to reduce the re-admission of patients effectively. According to a report, the readmission of patients cost US hospitals $40 billion annually. The Grady Hospital, the largest public hospital in Atlanta reduced readmission rates by 31% over a period of two years by adopting AI tools

Some of the prominent AI Healthcare applications

AI healthcare apps

–  Robot-assisted surgery process

–  Virtual nursing assistant

–  Virtual health assistant

–  Customer service bot

–  Administrative workflow assistant

–  Fraud detection

–  Drug creation

–  Digital consultation

–  Dosage error detection

–  Automated image diagnosis

–  Preliminary diagnosis

–  Diagnostic imaging interpretation

–  Health monitoring

9 Significant use cases of AI in healthcare

AI healthcare app testing

1. Derive new treatments based on patterns:

Many AI apps are being used for early disease diagnosis, customized treatment protocol development, etc. Some of the AI-based apps analyze the relationships between treatment techniques used and the patient outcomes and help to derive new treatment processes by analyzing old patterns.

2. Cure neurological disorders:

Neurological diseases and trauma to the nervous system can take away some of the patient’s abilities such as speech, movement, and even patient’s interaction with their surrounding environments. Specifically, to treat such conditions, AI-based Brain Computer Interfaces (BCIs) are found helpful to cure such issues.

3. Discover new drugs:

Many healthcare and pharma companies are leveraging AI to help with the discovery of new drugs. AI solutions help to discover and improve the earlier lengthy timelines and processes that were used with any new drug discovery and in a way helping to get new drugs to the market faster.

4. Identify high-risk patients early:

By analyzing vast amounts of historic patient data, AI-based solutions can provide real-time support to doctors and clinicians to help identify high-risk patients faster than before and help to save them with better medical care.

5. Deliver better radiology predictions:

Radiological images obtained by MRI machines, CT scanners, and x-rays offer non-invasive visibility into the inner working patterns of the human body. There are various deep learning platforms that are being leveraged by healthcare doctors to analyze unstructured medical data of blood samples, radiology images, etc. and these can give doctors better insights into a patient’s real-time needs directly by using various AI tools.

6. Simplify Electronic Health Records (EHRs):

Leveraging AI in EHRs helps to create more intuitive user interfaces that simplify workflows. Especially, some of the AI solutions are being used to automate patient data entry routine processes that usually consume a lot of human effort and time, thus easing the overall process and is helping to simplify EHRs.

7. Prioritize clinician’s tasks:

AI applications are also helping to process routine requests from the inbox, like medication refills and various other result notifications.  These AI apps also help to prioritize tasks that truly require the clinician’s attention, said Adam Landman, MD, Vice President, and CIO at Brigham Health, making it easier for users to work through their to-do lists.

8. Improvise cancer treatment:

There are many types of cancer being identified early by leveraging various AI apps. AI apps can spot subtle patterns that are likely to be missed by humans and helps guide through better cancer diagnosis of different stages of cancer and the probable risk in some patients.

9. Detect harmful bacteria in blood:

AI-enhanced microscopes are being used to scan for harmful bacteria in patient’s blood samples. It becomes easy to identify and predict harmful bacteria in the blood with up to 95% accuracy by using these AI-based microscopes.

Conclusion

AI is the technology that is being leveraged by every industry segment to enhance their business outcomes and also to deliver a better human experience. The impact of AI in healthcare has been much more and wider than when compared to other industry domains.

Today, AI applications and especially bots are used in various treatment plans, diagnose diseases faster, protect patients from diseases, help them with customized treatment plans, etc. AI-powered solutions are predicted to play a leading role in how healthcare systems of the future operate and augment clinical resources.

These AI apps are likely to influence patient outcomes greatly in the coming years. But, the healthcare providers need to make sure their AI-enabled apps work seamlessly and continue to deliver a great patient experience. Hence, to get fully functional AI apps, and chatbots, leverage AI-based testing by next-gen testing service providers to get the desired high-performing AI apps.

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