Importance of AI Testing in Healthcare Domain Applications

AI applications in healthcare are advancing rapidly, with potential applications being shown across various domains of medicine and healthcare. However, there are currently limited software testing tools and strategies available to test AI-based healthcare products.

This article explores the key areas, main challenges and limitations, and approaches of Software Testing of AI-based healthcare domains and considers the steps required to improve current testing strategies.

AI applications in healthcare are advancing rapidly, with potential applications being shown across various domains of medicine and healthcare. However, there are currently limited software testing tools and strategies available to test AI-based healthcare products.

This article explores the key areas, main challenges and limitations, and approaches of Software Testing of AI-based healthcare domains and considers the steps required to improve current testing strategies. 

The growing potential of artificial intelligence in healthcare

Artificial Intelligence and Machine Learning seem to be the new slang of the 21st century. PwC, a professional services firm predicts that AI will add $16 trillion to the global economy by 2030. AI-ML are general purpose technologies capable of affecting entire economies. It outshines in recognizing micro and macro patterns insignificant to humans and can be very useful. Ever since the probability and idea of making machines learn by themselves came into existence, its applications have been used in almost every sector of the economy and the healthcare industry is no exception.

AI now has the ability to find patterns in massive volumes of data that are too complex for humans to notice. It accomplish this by merging data from a range of sources, including linked home devices, medical records, and increasingly non-medical data.

AI is finding huge recognition in the field of healthcare and medical diagnostics for the past few decades. However, one section in the healthcare industry that is relatively new to the use of AI is the verification and validation of medical devices. With the demands placed on testing applications and reliability towards delivery teams increasing exponentially over the years, it has become more important to take a step beyond just automation and start using AI and ML for medical device testing.
 

Testing of medical devices is a long and important process that should be carried out simultaneously throughout the development process. Integrating AI and ML into this process can be advantageous in many ways, and here are seven major areas that can gain the most from it:

1. Data-driven insights:  As more and more data is being made available for general processing and data insight generation, decision science is now mostly driven by calculating usage of AI and ML. Platforms and tools for medical device testing are becoming increasingly available to ferment data in a short period and derive meaningful insights, making it available in real-time.  These AI tools can be used during product verification and validation to identify complex scenarios for testing from the requirement traceability matrix.
 

2. Creating test cases: Test cases are mostly designed by highly skilled test and automation engineers. This needs a combination of versatile skills and collaborative effort across teams. By using AI tools test cases can be generated automatically which takes multiple factors into consideration like functionality, scalability, coverage, loading.  AI algorithm has the ability to look inside the code to derive test cases that have a higher probability to uncover defects compared to the manual approach. The use of AI has led to powerful increase in the pace of test development.

3. Bringing intelligent automation to testing: Instead of running tests and fixing the bugs manually AI-driven test controllers can be used to identify test case failures and run repetition steps to cover multiple regression cycles in accordance with the type of fault detected. It helps to increase the automation coverage by approximately 30% when using AI.         

      4. Improving system agility: One of the primary reasons why automated tests fail is not for their lack of quality but the lack of their swiftness keeping with the changes that are taking place. AI-powered testing tools can be designed to learn from test data generated using the emerging Machine Learning process so that test automation systems can adapt quickly to system changes. 

5. Self-healing capability: Testing is a continuous process in a software life cycle. Organizations spend around 15 to 25% of their time maintaining automated test cases. A self capable system driven by AI can be a great tool to reduce the burden on an ever increasing testing budget as the system grows to be more complex.  It is usually observed that about 60 to 70% of all defects reported can be addressed by employing AI-powered solution.

6. Minimizing manual labor: Manual testing of medical devices can be an laborious task as it involves several regulative requirements. AI helps to reduce manual testing efforts at some steps by bringing analytic functions using a combination of image and other sensors hence improving the speed and accuracy of testing. It has been found that the use of AI in testing reduces product maintenance costs by almost 40%.

7. Strict testing procedures to prevent diagnostic errors: Diagnostic errors lead to 60% of all medical errors. As AI can offer more accurate diagnostics there is always a chance that it can also make mistakes, which sometimes causes companies to hesitate about adopting AI in diagnosis. The use of AI and ML in medical device testing has its pros and cons, however its benefits outweigh some of the challenges associated with it.

Software testing in AI and ML

The core element of developing ML and AI algorithms are testing. You may compare this with common unit testing of the application testing. The AI/ML engineers develop an AI algorithm and verify that the training data does a qualified job of accurately classifying or regressing data with good generalization. Test Engineers also use some validation techniques which are like test data of software testing.

AI-based software uses algorithms and data which are mainly working together to show the results. If the algorithm's validation phase gets wrong parameters then it might affect the results which we are looking for. To get more accurate results the test engineers needs to revisit the algorithms themselves, change the parameters if required and rebuild the model. This might be compared to the system test which the tester was doing to understand the behaviours of the system.

Testing approach on AI-ML based Healthcare Domains

A common healthcare domain testing is a process to check healthcare applications with factors like safety, compliance, accuracy and cross dependency with other entities, etc. The tester ensures that the standard quality, reliability, performance, safety, and efficiency of the Healthcare application on its place and software behave as expected. Current AI-based tools and software accompany algorithms and logical tests which Al engineers already did.

However, the challenging part for the tester is to check how the algorithm behaves within the software and therefore the system. QA teams got to have strong domain knowledge and backgrounds on healthcare systems, algorithms and the way these two works together.

Mostly healthcare algorithms are pretty complex and challenging to predict for common software testers. The algorithm goes through training and testing sets thus creating some meaningful data associating with human behaviours. An insufficient or incomplete data set or low-quality data can cause biases within the solution. A system is over-trained to ascertain the same thing or isn't trained enough to form an accurate judgment.

Another challenge that testers face while they're testing AI-based medical systems is that the amount of data required to test the system. Approaching restricted data items won't provide statistical assurance of the system. That opens another challenge for testers on what kind of skills should a tester have and the way they should interact with complex systems.

Mainly testers are using boundary testing and dual coding to resolve most of the problems associated to complexity. Testers got to have some data knowledge and familiarity with Algorithms would be an essential skill.

Sometimes the algorithm used, huge data volumes or solution complexity, testing these systems are often as complex as the solutions itself. It requires extensive technical and data science expertise from the testers making the AI tester's job different from any other manual or automation testers.

Conclusion

Software industries may face a spread of challenges when using AI to test the healthcare applications or medical devices for quality, including identifying the precise cases, a scarcity or lack of understanding about what really must be done. Verifying applications behavior based on data input, testing application for functionality, performance, scalability, security, and more.

In conclusion, AI-based healthcare products will get complicated day by day and as testers we got to be ready to test one of the most complex algorithms and logic, potentially saving lives and protecting the people.