The role of explainable ai in in vitro diagnostics according to European regulations: ai is becoming increasingly important in healthcare, especially in in vitro diagnostics (IVD). The European IVDR recognises software, including ai and ML algorithms, as part of IVDs. This regulatory framework presents significant challenges for ai-based IVDs, particularly those using DL techniques. These ai systems must perform accurately and provide explainable results to meet regulatory requirements. Trustworthy ai is essential as it must enable healthcare professionals to confidently use ai in decision-making, which requires the development of explainable ai (xAI) methods. Tools such as layered relevance propagation can help visualise the elements of a neural network that contribute to specific outcomes, providing the necessary transparency.
The IVDR outlines rigorous criteria for developing and evaluating ai-based in vitro diagnostics, including scientific validity, analytical performance, and clinical performance. As ai becomes more integrated into medical diagnostics, it is critical to ensure the transparency and traceability of these systems. Explainable ai addresses these needs by making the decision-making process of ai systems more understandable to medical professionals, which is critical in high-risk environments such as medical diagnostics. The focus will be on developing human-ai interfaces that combine the computational power of ai with human expertise, creating a synergy that improves diagnostic accuracy and reliability.
Explainability and scientific validity of ai for in vitro diagnostics:
The IVDR describes scientific validity as the link between an analyte and a specific clinical condition or physiological state. When applying this to ai algorithms, results should be explainable rather than simply produced by an opaque “black box” model. This distinction is important for validated diagnostic methods and the ai algorithms that support or replace these methods. For example, an ai system designed to detect and quantify PD-L1-positive tumor cells should provide pathologists with a clear and understandable process. Similarly, in colorectal cancer survival prediction, the features identified by ai should be explainable and supported by scientific evidence, requiring independent validation to ensure that the results are reliable and accurate.
Explainability in analytical performance evaluation for ai in IVD:
When evaluating ai analytical performance in in vitro diagnostics, it is critical to ensure that ai algorithms accurately process input data across the intended spectrum. This includes taking into account patient population, disease, and scan quality. Explainable ai (xAI) methods are key to defining valid input ranges and identifying when and why ai solutions may fail, particularly on data quality issues or artifacts. Proper data governance and a comprehensive understanding of training data are essential to avoid bias and ensure robust and reliable ai performance in real-world applications.
Explainability in clinical performance assessment for ai in IVD:
Clinical performance evaluation of ai in in vitro diagnostics assesses the ability of ai to provide relevant results for specific clinical conditions. xAI methods are crucial to ensure that ai supports decision-making effectively. These methods focus on making the ai decision process traceable, interpretable, and understandable to medical experts. The evaluation distinguishes between components that provide scientific validation and those that clarify medically relevant factors. Effective explainability requires static explanations and human-centered interactive interfaces that align with the needs of experts, enabling deeper causal understanding and transparency in ai-assisted diagnoses.
Conclusion:
For ai solutions in in vitro diagnostics to fulfil their intended purpose, they must demonstrate scientific validity, analytical performance and, where relevant, clinical performance. To ensure traceability and reliability, explanations need to be reproducibly verifiable by different experts and technically interoperable and understandable. xAI methods address fundamental questions: why the ai solution works when it can be applied and why it produces specific results. In the biomedical field, where ai has great potential, xAI is crucial for regulatory compliance and for empowering healthcare professionals to make informed decisions. The article highlights the importance of explainability and usability to ensure the validity and performance of ai-based in vitro diagnostics.
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Sana Hassan, a Consulting Intern at Marktechpost and a dual degree student at IIT Madras, is passionate about applying technology and ai to address real-world challenges. With a keen interest in solving practical problems, she brings a fresh perspective to the intersection of ai and real-life solutions.
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