Transforming quality assurance in healthcare using GenAI

The global MedTech software market is projected to reach $598.90Bn by 2024 growing 5.3 percent annually due to increased R&D investments. As the market shifts towards tech-first patient care, MedTech software must meet quality and regulatory standards to ensure effective care and patient safety, making Quality Assurance (QA) critical throughout the Software Development Life Cycle (SDLC). QA ensures reliability, functionality, and adherence to industry standards with MedTech companies dedicating 31 percent of their software budget to QA and testing.

Artificial Intelligence (AI) tools have enhanced healthcare QA efficiency -- GenAI is notably reducing manual testing, improving software usability, and enhancing code quality. AI adoption is expected to make software testing more autonomous, boosting QA productivity by nearly 20 percent, with GenAI tools projected to write 70 percent of software tests by 2028.

The game-changing possibilities with GenAI in QA

GenAI empowers developers and QA teams with various tools that enhance test data generation, test scenarios exploration, anomaly detection and system testing for overall software quality.

• Synthetic data generation (SDG)

Near real synthetic data can be generated such as patient vitals from medical devices, or medical images, or even patient histories in Electronic Health Record (EHR).Integrated GenAI tools automate the creation of diverse test cases and ensure authentic results while addressing ethical concerns associated with using real patient data.

• Scenario exploration

GenAI expands coverage of testing scenarios by identifying edge cases and rare clinical situations – thus reducing manual testing. GenAI tools generate complex scenarios that accurately mimic real-world usage patterns and create near real data where the data is unavailable or difficult to obtain.

• Anomaly detection

GenAI generated test cases can quickly adapt to evolving software requirements. GenAI tools use a self-learning feedback loop to proactively identify anomalies and potential vulnerabilities, increasing overall software quality, reliability, and stability.

• Complex system testing

GenAI explores edge cases by introducing extreme data inputs, replicating uncommon user behaviors, and simulating unexpected system failures. This has become a critical part of comprehensive software testing for complex systems.

• Regulatory documentation

GenAI tools can automate the generation of extensive test documentation required for verification and validation of regulated MedTech software products. GenAI has the potential to significantly accelerate generation of regulatory documentation.

From aiding in comprehensive requirements analysis, enhancing testing coverage, optimizing test design to accelerating reporting, GenAI has the potential to significantly accelerate quality compliance and improve user satisfaction.

Driving patient safety and data privacy

While GenAI tools can enhance QA for MedTech software, there are few concerns around patient safety and data privacy which must be addressed. Here are few steps to mitigate concerns responsibly:

• Transparency and explainability

Provide clear explanations behind each test case and allow human testers to assess its purpose and potential impact.

• Biased outcomes

Employ a two-step approach to mitigate biases in AI. Either leverage diverse training datasets to represent the full spectrum of patients and healthcare scenarios or use fairness metrics to evaluate and mitigate bias in generated test cases, ensuring unbiased test coverage.

• Optimal test coverage

Regularly evaluate GenAI-generated test suites to capture a broad spectrum of scenarios for getting a positive result in complex testing. Augment GenAI-generated test suites with human-designed test cases to add additional level of security.

• Re-testing practices

QA teams can apply AI-powered prioritization techniques to fulfill regression testing efforts. Striking the optimal balance between automation powered by AI and human intuition is necessary.

Conclusion

The brimming MedTech software market underscores the ever-increasing need for high-quality regulatory compliant software. Leveraging GenAI can augment human expertise for a more proactive and agile QA process. The process is simple: finalize the vision and scope for integrating GenAI tools and upskill engineering teams to transform the way they conduct QA activities.

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Dhaval Shah is EVP, CitiusTech, a firm that specializes in combining human expertise with GenAI to build future proof MedTech software. CitiusTech has expertise in responsibly transforming quality assurance tailored to MedTech workflows. and focuses on delivering reliable, secure, high-quality MedTech solutions to benefit not just the patients, but the entire healthcare ecosystem.