Explainable AI in Life Sciences: Understanding the ‘Why’

Explainable AI is a pivotal advancement in the dynamic field of life sciences and addresses the urgent need for transparency in artificial intelligence (AI) systems. By providing tools and frameworks to clarify the underlying reasoning behind AI predictions, Explainable AI plays a critical role in improving the reliability and ethical integrity of AI applications in biological and clinical research, medical practice and patient care. Despite the incredible benefits of AI integration, the “black box” nature of these systems has led to significant concerns about their decision-making processes. The inclusion of explainable AI is therefore crucial to demystify these processes and ensure the responsible use of AI in the life sciences.

Explainable AI in Practical Use

To address the challenges of gaining trust in AI systems and explaining their decisions, the concept of Explainable AI (XAI) has recently emerged as a critical field of study [2]. XAI can be implemented through various techniques and methodologies depending on its use case. Some example concepts are explained in the following:

  1. Feature Importance Methods [3]: These methods highlight which features (variables) in the input data most significantly impact the output of the model. For example, in a medical diagnosis AI, feature importance methods can reveal which symptoms or test results are most influential in determining a diagnosis.
  2. Visualization Techniques [4]: Visualization provides a more intuitive understanding of the data and model behavior. Techniques like t-SNE (t-distributed Stochastic Neighbor Embedding) [10] or PCA (Principal Component Analysis) [11] are used to visualize high-dimensional data in a more comprehensible two or three-dimensional space.
  3. Attention Mechanisms in Neural Networks [5]: Similar to the above, Attention Mechanisms are highlight parts of the input data (like specific words in a text or areas in an image) that the model focuses on when making predictions. For example, in genomic data, attention mechanisms can highlight particular genes or mutations that are influential in disease prediction.

Integration of Domain Knowledge

Explainable AI in the life sciences is no longer limited to purely algorithmic approaches. Instead, it is becoming more common to integrate domain-specific knowledge and biological context into AI models [6]. For example, if experts know that certain genetic markers are strongly associated with a disease, these rules can be explicitly included in the model. Additionally, life science experts can identify and rectify anomalies or outliers in the data, which might otherwise lead AI models to make incorrect inferences.

By combining the computational power of AI with expert knowledge, researchers can create more robust and interpretable models that provide meaningful explanations in the context of life science [7]. This fusion of explainable AI and expertise has the potential to accelerate discoveries in areas such as personalized medicine and biomarker identification.

Regulatory Compliance and the EU AI Act

In the evolving landscape of (explainable) AI in the life sciences, regulatory compliance takes center stage. Recent trends show a growing awareness of the importance of responsible use of AI in research and clinical applications.

The new EU AI Act will have a significant impact on the development and use of transparent AI systems in the field of life sciences. By focusing on transparency and human oversight, it aims to ensure that AI technologies are used safely and ethically, especially in high-risk areas such as life science as a whole. While some argue that the AI Act does not explicitly state requirements for XAI or banning the use of black-box AI systems [1], it poses challenges, particularly in terms of the pace of innovation and the burden on AI developers and companies. This is because the strict legal requirements and the need for extensive documentation and compliance checks could significantly increase the complexity and cost of developing and implementing AI systems.

Real-world Applications of Explainable AI

Understanding the AI decision-making process is becoming increasingly prevalent in real-world applications in the life sciences. Recent research includes AI-assisted drug repurposing, where explainable AI plays a central role in identifying existing drugs that can be repurposed for new therapeutic uses [8]. In addition, XAI helps in the discovery of biomarkers for rare diseases [9], giving patients hope for more accurate and timely diagnoses. These practical applications demonstrate the transformative potential of explainable AI in addressing some of the most pressing challenges in the life sciences.


The Vital Role of Explainable AI in Life Sciences

In summary, explainable AI is revolutionizing the life sciences by improving transparency and understanding in AI-driven processes. Its application in genomics, drug discovery and biomarker identification, coupled with the integration of domain-specific knowledge, accelerates discoveries, and provides deeper insights into complex biological systems. Ethical considerations and regulatory compliance, especially the EU AI Act, ensure the responsible use of AI and underline the transformative potential of XAI in practical applications such as drug repurposing and rare disease diagnosis.

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[1] EU AI Act argumentation: https://dl.acm.org/doi/pdf/10.1145/3593013.3594069

[2] Explainable AI Survey: https://ieeexplore.ieee.org/document/8466590

[3] Feature Importance and visualization: https://arxiv.org/pdf/2101.09429.pdf

[4] A survey of visual analytics for Explainable Artificial Intelligence methods: https://3dvar.com/Alicioglu2021A.pdf

[5] A General Survey on Attention Mechanisms in Deep Learning: https://ar5iv.labs.arxiv.org/html/2203.14263

[6] A review of some techniques for inclusion of domain-knowledge into deep neural networks: https://www.nature.com/articles/s41598-021-04590-0

[7] Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions: https://www.nature.com/articles/s42256-020-00232-8

[8] Extending the Nested Model for User-Centric XAI: A Design Study on GNN-based Drug Repurposing: https://osf.io/preprints/osf/yhdpv

[9] The benefits and pitfalls of machine learning for biomarker discovery: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10558383/

[10] t-Distributed Stochastic Neighbor Embedding: https://www.sciencedirect.com/science/article/pii/S1874778719301746

[11] Principal component analysis: https://royalsocietypublishing.org/doi/10.1098/rsta.2015.0202

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