Embracing the GenAI potential: Upgrading QA processes for healthcare
By Siddharth Sheth, Sr. Vice President, Head of Engineering & Quality Practices, CitiusTech
The healthcare domain is undergoing a seismic paradigm shift, with the advent of digital health. The ripples caused by this shift have also been seen in India. So much so that the digital health market in India is projected to grow significantly, with the market size estimated to reach $18.34 billion by 2030, up from $3.83 billion in 2022, representing a compound annual growth rate (CAGR) of 21.6% during the forecast period of 2022-30.
Whether India or globally, with digital health being the new norm, the market for medical software is only expected to grow – making quality assurance (QA) integral for ensuring the reliability, functionality, and quality of these platforms.
There’s scope for innovating how enterprises approach QA across the Software Testing Life Cycle (STLC) by leveraging emerging technologies like Generative AI (GenAI). The ultimate objective is to save time, money, and effort while achieving accuracy, improving patient-centricity, speeding time to market, and ensuring compliance adherence.
IDC’s bold prediction for the APEJ region indicates that by 2028, GenAI tools will be expected to write a whopping 70% of software tests. This can potentially revolutionise the testing landscape, not just by automating tasks but also by significantly improving test coverage, software usability, and even code quality.
How GenAI can help QA
Software testing accounts for 50-60% of the total cost of software development including data verification, functional testing, automated verification, and others. GenAI can help improve the QA process and reduce the overall cost by managing resources efficiently, finding patterns, spotting unusual activity, and predicting potential problems.
Compared to the traditional testing process, GenAI’s cognitive approach facilitates predictive analysis, empowering developers and QA teams to enact preventative measures and enhance the software’s overall quality.
Domains where GenAI helps drive better results:
Synthetic data generation (SDG)
Leveraging GenAI, QA teams can create diverse, representative datasets resembling real-world scenarios – for unbiased testing and zero ethical dilemmas associated with real patient data. Using synthetic data for software quality assurance has multiple applications, such as generating diverse test cases to assess the software’s behaviour, performance, and resilience to deviations. This proactive approach helps detect deviations before they become more significant problems in the early testing phases. In fact, Gartner predicts that 60% of AI data in 2024 will be synthetic, aimed at simulating reality or projecting future scenarios.
Scenario exploration
QA teams can explore a broad spectrum of potential scenarios with GenAI. They can uncover edge cases and rare clinical situations that might have been overlooked during manual testing with fixed datasets. GenAI can also create complex scenarios that mimic real-world usage patterns, helping QA teams evaluate how the software responds to different user inputs and system states.
Continuous monitoring and analysis
While traditional AI relies on defined rules and human-programmed instructions, GenAI is more dynamic. In addition to creating test cases based on evolving requirements, it allows continuous monitoring and analysis to detect anomalies and potential bugs in the software automatically.
GenAI establishes a continuous feedback loop, learning from the results of previous tests and interactions to adjust its testing strategies accordingly. This self-learning capability improves its adaptability over time as it becomes more efficient in detecting vulnerabilities and areas for improvement in the software.
Complex system testing
GenAI excels at simulating real-world scenarios or and exploring edge cases (in the form of performance bottlenecks or scaling issues) for complex testing. The software interacts with various components, systems, and environments during simulation-based tests, including complex user workflows, third-party integrations, network conditions, and data interactions. This simulation flags potential issues that may arise in production environments in real-time.
On priority: Leveraging GenAI to address patient safety and privacy challenges
Before implementing GenAI for healthcare software QA, it’s important to scrutinise potential patient safety and privacy challenges. Here’s how careful consideration can mitigate these concerns responsibly:
- Ensuring transparency and explainability: Healthcare enterprises can ensure GenAI-generated test scenarios are transparent and understandable. This can be realised by explaining the rationale behind each test case and allowing human testers to assess its purpose and potential impact.
- 2. Protecting patient data: To safeguard patient data, organisations can utilise privacy-preserving techniques for generating diverse test data. QA teams can implement privacy and security measures in the design phase, ensuring that patient data is anonymised, and synthetic data adheres to privacy regulations.
- Mitigating bias: To tackle AI bias, the QA team must continuously monitor and address potential biases in AI algorithms used for test generation by employing diverse training datasets to represent the full spectrum of patients and healthcare scenarios. They can also utilise fairness metrics to evaluate and mitigate bias in generated test cases, ensuring unbiased test coverage.
- Maintaining optimal test coverage: To enhance test coverage, QA teams must regularly evaluate GenAI-generated test suites and supplement them with human-designed test cases to ensure comprehensive coverage. Deploying a blend of GenAI-generated and human-designed test cases helps leverage both approaches’ strengths for optimal test coverage.
- Enacting re-testing practices: For agile application release, QA professionals must reimagine proactive regression testing to minimise time and resources for training. This can be followed by implementing robust change impact analysis to identify areas of the software that require regression testing after code changes. The approach helps focus testing efforts on relevant sections and minimises unnecessary testing. QA teams can also apply AI-powered prioritisation techniques to prioritise regression testing efforts based on the potential impact of changes on critical functionalities, ensuring efficient resource usage.
The GenAI imperative
The soaring medical software development market in India underscores the criticality of high-quality healthcare software. This reflects the need for full-proof quality assurance in healthcare and the potential of GenAI in transforming the QA process. However, enterprises need to approach this with caution.
Embracing GenAI should be about augmenting quality assurance rather than replacing manual testing. Enterprises can make the process more proactive and agile by infusing human judgement and critical thinking into GenAI-governed quality assurance.
In the coming years, as GenAI continues to further trickle down into the medical software domain, its impact will be felt across diverse healthcare settings worldwide. By fostering collaboration between technologists, healthcare professionals and regulatory authorities, we can harness the full potential of generative AI to revolutionise the QA process and pave the way for a healthier, more informed future.
