Content 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.

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

1. Better patient outcomes:

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:

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:

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:

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:

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:

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:

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 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

9 Significant use cases of AI in healthcare

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.

The post Role of AI in The Healthcare Sector first appeared on TestingXperts.

Artificial Intelligence (AI) is the latest technology to be leveraged across all industries and domains. AI continues to make its way into domains previously reserved for human skills and has been enabling significantly dominant results. On a similar note, AI is being leveraged in the field of software testing also to ease the automation testing process and deliver more quality outcomes.

Content 1.  What is AI-based Testing? 2.  How has AI evolved in software testing? 3.  Some of the benefits of leveraging AI in software testing 4.  What are the 4 key AI-driven testing approaches? 5.  What are the 4 main categories of AI-driven testing tools?   5.1  Differential tools   5.2  Visual AI testing tools   5.3  Declarative tools   5.4  Self-healing tools 6.  Conclusion

According to the World Quality Report 2020-2021, it is stated that the interest in AI-based testing is increasing, and approximately 90% express that their organizations are focusing on significant growth in the areas of AI testing and testing of AI. Additionally, around 80% of respondents plan to augment the number of AI-based trials and proofs of concept.

Typically, the application of AI in software testing will make the entire testing process faster, clearer, easier, and budgeted. Therefore, AI-based testing will provide a strategic platform where software testers can leverage AI and take the testing process to a new level and thus deliver more quality results to businesses.

Test automation tools have been supporting the continuous testing process for many years.  But, now the need for more effective tools and techniques such as embedding AI in software and QA testing has become a mandate for delivering quality software and for ensuring superior customer experience.

What is AI-based Testing?

AI-based testing is a software testing technique in which AI and Machine Learning (ML) algorithms are used to effectively test a software product. The objective of AI-based testing is to make the testing process smarter and highly effective. With the inclusion of AI and ML in testing, logical reasoning and problem-solving methods can be applied to improve the overall testing process.  Further, in this testing method, AI testing tools are used to execute the tests that use data and algorithms to design and perform the tests without any human intervention.

How has AI evolved in software testing?

The paradigm of software testing has evolved significantly over the past two decades. Right from manual testing to automation testing, where selenium is considered to be one of the finest test automation tools, the testing journey has been quite encouraging. However, in today’s fast-paced IT world, the domain of software testing has to come up with innovative and well-researched testing methodologies. For this purpose, the dawn of AI-based testing has come up and has been proving very impactful.

AI algorithms can completely mimic human intelligence, and ML allows computers to learn automatically without any human intervention. Interestingly, AI and ML involve the development of unique and specific algorithms that can access data, learn from that data by extracting patterns to make decisions, and these predictions are to be used in software testing effectively.

Moreover, enterprises are rushing towards tools that can leverage AI and ML algorithms and can be used for testing the software effectively. It has also been seen that businesses get many benefits from AI-based testing as it will enable faster and continuous testing, complete automation without any human intervention, and also enables quicker ROI.

Some of  the benefits of leveraging AI in software testing

Visual validation –

AI has pattern recognition and image recognition capabilities that together help to detect visual bugs by performing visual testing on applications. It helps to make sure that all the visual elements are engaging and can function properly. AI can recognize dynamic UI controls irrespective of their size, shape and analyses them at a pixel level.

Improved accuracy –

In the manual testing method, the chances of human-prone errors are high, especially in situations of repetitive tasks. Automation testing helps in removing these human-prone errors. But, with the advent of AI in automation testing, repetitive tasks are handled more effectively and the results are recorded more accurately. Thus, AI helps in removing the minute chances of errors and improves the overall accuracy of tests.

Better test coverage –

AI in testing increases the test coverage as it can check the file contents, data tables, memories, and internal program states seamlessly. It also helps to determine if the program works as expected and delivers effective test coverage.

Saves time, money, and efforts –

Software tests need to be repeated whenever there is an instance of change being made in the source code. Manually this becomes time-consuming and takes a lot of effort from testers. But, with AI-driven tests, repetitive tasks are handled properly, quickly, and efficiently.

Faster time-to-market –

AI-driven tests support continuous testing, and thus products are released faster which helps businesses go early-to-market.

Reduces defects –

AI in testing helps in early and fast bug identification, which ultimately reduces the defects and makes the product bug-free, and reliable for end-users.

What are the 4 key AI-driven testing approaches?

Differential testing – Differences are classified and application versions over each build are compared in this type of testing.

Visual testing – Visual testing is a software testing technique in which the look and feel of an application are tested by leveraging image-based learning and screen comparisons.

Declarative testing – It aims at specifying the intent of the test in a natural or domain-specific language. The system decides how to perform the test

Self-healing automation –Element selection in tests is auto-corrected when the UI changes.

What are the 4 main categories of AI-driven testing tools?

Differential tools

These tools leverage AI and ML algorithms to identify code-related issues, security vulnerabilities, regressions, etc. This is achieved through code scanning, unit test automation, etc. Some of the tools under this category are:

1. Launchable:

Launchable is based on an ML algorithm that predicts the likelihood of failure for each test based on past runs and whenever the source code changes under test. This tool lets the user record the test suite so that tests that are likely to fail are run first. One can choose this tool to run a dynamic subset of tests that are likely to fail, thereby reducing a long-running test suite to a few minutes.

2. Google OSS-Fuzz:

It is a fuzz testing tool that aims to make common open-source software more secure, stable, and reliable. This tool combines modern fuzzing techniques with scalable, and distributed execution. This tool supports C/C++, Rust, Go, and Python code

Visual AI testing tools

With the ever-growing number of platforms that vary in screen sizes and have different configurations, it has become a tedious task for test engineers and developers to effectively test the UI layer. Also, the UI layer experiences constant changes from time-to-time as businesses wish to provide a better user experience. Therefore, today there is a dire need for visual AI testing tools that effectively test all variations of these UI layers. Some of the tools that fall under this category are:

1. Applitools:

This is an AI-powered visual testing and monitoring platform. This has been named a next-generation test automation platform powered by Visual AI. The major features include Applitools Eyes which helps to increase test coverage and reduce maintenance. The Ultrafast grid helps with cross-browser and cross-device testing and accelerates functional and visual testing by 30 times. This Applitools platform integrates with all modern test frameworks and works with many existing testing tools like Selenium, Appium, Cypress, etc.

2. Percy by BrowserStack:

It is an all-in-one visual review platform that comes with amazing features such as pixel-by-pixel diffs, responsive diffs, and snapshot stabilization. This tool allows cross-browser rendering, high-speed rendering, and has parallelization capabilities. Percy helps teams automate visual testing. This Browserstack tool is used to typically capture screenshots and compare them against the baselines and display visual changes. It increases the visual coverage and helps teams to deploy code changes with confidence.

Declarative tools

These tools aim to enhance test automation productivity and stability. These tools leverage AI and ML and have significant abilities related to Robotic Process Automation ( RPA), Natural Language Processing (NLP), Model-based Test Automation (MBTA), and Autonomous Testing Methods (AT). The main aim of these methods is to eliminate tedious, error-prone, repetitive tasks through smart automation. Some of the tools that fall under this category are:

1. Tricentis:

This is an AI-driven, next-gen automation testing tool that allows Agile and DevOps teams to rapidly achieve test automation goals. It allows teams to go beyond continuous testing with AI. It allows automating end-to-end testing of software applications. This tool combines multiple aspects of software testing (test case design, test automation, test data design and generation, and analytics) to test GUIs and APIs from a business perspective.

2. UiPath Test Suite:

This is the latest Test Suite that can be used to automate and centralize the testing process and helps to launch resilient robots and ensures high-quality of every automation. The UiPath Test Suite consists of UiPath Studio Pro, UiPath Test Manager, and UiPath Orchestrator. Thus, UiPath test Suite can be used to automate tests in UiPath Studio Pro with drag and drop interfaces, helps to manage tests with UiPath Test Manager, and also helps to execute tests witn UiPath Orchestrator. Therefore, UiPath Test Suite is helping businesses with a 360 degree testing and is helping RPA developers to build more, test better, and fix never.

Self-healing tools

In automation tests, the problem of flakiness, reliability, and maintenance issues persist, and this is one of the main reasons why AI and ML have been introduced in test automation. To overcome these problems, self-healing tools have been developed that are mostly based on a record and playback mechanism, wherein the main ML engine resides in the self-healing of the recorded scripts. Some of the tools that fall under this category are:

1. Mabl:

It is the leading intelligent test automation platform built for CI/CD. Mabl crawls your app screens and begins to run default tests that are common for most applications. It also uses ML algorithms to improve test execution and defect detection. 

2. Testim:

This tool uses AI and ML algorithms to automate testing to its full extent. AI is used to speed up the authoring, execution, and maintenance of the tests. Testim includes self-maintenance of automated tests that are ML-based. This results in the fast authoring of stable automated tests.

Conclusion

Today as technology is advancing, more and more businesses are adopting agile and DevOps practices. But, with the adoption of these methodologies, the need for robust tools that support continuous testing and continuous release are also rising. This is where AI-based test automation tools come into the picture. With the advent of AI in software testing, businesses are now able to achieve faster tests and reliable products.

AI-driven test automation tools not only support DevOps practices but also bring in human-like decision-making abilities which ultimately helps in releasing high-quality software in less time. Leverage next-gen AI-based testing services by next-gen testing services provider to get faster and quality releases with more efficiency and accuracy.

The post AI in Software Testing – Benefits, Approaches, Tools to Look in 2023 first appeared on TestingXperts.