< + > Tell Me Why: The Imperative of Explainability in AI for Healthcare

The following is a guest article by Neeraj Mainkar, VP of Software Engineering and Advanced Technology at Proprio

Artificial Intelligence is revolutionizing healthcare by enhancing diagnostic accuracy, personalizing treatment plans, and it can potentially improve patient outcomes. However, the rapid demand for AI integration into healthcare systems raises significant concerns about the transparency and explainability of advanced technologies. In a domain where decisions can mean the difference between life and death, the ability to understand and trust AI decisions is both a technical requirement and an ethical imperative.

Understanding Explainability in AI

Explainability refers to the ability to understand and articulate how an AI model arrives at a particular decision. In simple AI models, like decision trees, this process is relatively straightforward. However, in complex deep learning models with numerous layers and intricate neural networks, tracing the decision-making process becomes nearly impossible. Reverse engineering or examining specific issues within the code is exceedingly difficult. When a prediction doesn’t come out as expected, pinpointing the reason can be challenging due to the complexity of these models. Even the creators can’t always explain their behavior or outputs.

This lack of transparency, or the “black box” nature of AI, is a significant concern in the healthcare environment, where understanding the rationale behind an AI-informed treatment or diagnosis output has incredibly high stakes due to the human lives involved.

The Significance of Explainability in Healthcare

The push for AI in healthcare is driven by its potential to enhance diagnostic accuracy and treatment planning. Understanding the decision-making process of AI and ensuring its explainability is a top priority before it can be implemented in a healthcare setting. This need for explainability is multifaceted:

• Patient Safety and Trust: Patients and healthcare providers must trust AI-driven decisions; without explainability, trust diminishes, and the acceptance of AI in clinical settings becomes challenging
• Error Identification: In healthcare, errors can have severe consequences; explainability allows for the identification and correction of errors, ensuring the reliability of AI systems
• Regulatory Compliance: Healthcare is a highly regulated industry; for AI systems to be approved and used, they must meet stringent regulatory standards that often require a clear understanding of how decisions are made
• Ethical Standards: Transparency in AI decision-making aligns with ethical standards in healthcare, ensuring that decisions are fair, unbiased, and justifiable

There also are significant financial ramifications related to explainability. Research indicates that companies deriving at least 20% of their earnings from AI are more likely to adhere to best practices for explainability. Additionally, organizations that cultivate digital trust through transparent AI practices are more likely to benefit from annual revenue and earnings growth rates of 10% or more.

Challenges in Achieving Explainability

Achieving explainability in AI in healthcare presents several challenges, with the primary hurdle being the inherent complexity of AI models. The more accurate and dense a model is, the less explainable it becomes. This paradox means that while complex models may provide highly accurate results, their decision-making process remains opaque.

Another challenge is balancing performance and explainability. Simplifying models to enhance interpretability often reduces accuracy. In a complex healthcare environment where every detail is crucial for disease prediction or diagnosis, models should not be simplified, as preserving their complexity is vital.

Towards Solutions: Research and Collaboration

Explainability is something all AI companies are grappling with. Significant research efforts are underway to unravel the inner workings of large language models and to understand the reasoning behind their generated responses. Recently, Anthropic researchers made progress in making AI models more understandable. They extracted millions of features from one of their production models, demonstrating that interpretable features do exist and are important for safety, guiding model behavior, and classification.

While this progress is encouraging, there is still much to uncover, particularly in understanding AI’s operation within the healthcare environment. Because of this, organizations should prioritize transparency and continue to be forthcoming about their research efforts. For instance, the MIT IBM Watson research lab, Google, and many others are making strides in this area. Additionally, there are several approaches that can be explored to enhance explainability:

• Interpretable AI Models: Developing models that are inherently more interpretable, using techniques like attention mechanisms and feature importance
• Stakeholder Engagement: Involving healthcare professionals, ethicists, regulators, and AI researchers in the development process to ensure diverse perspectives and needs are considered
• Education and Training: Improving AI literacy among healthcare professionals and the general public to create a better understanding of AI decision-making processes
• Regulatory Frameworks: Establishing robust regulatory frameworks and ethical guidelines to ensure AI systems are transparent and accountable

The Road Ahead

While research efforts to achieve fully explainable AI in healthcare are ongoing, it is a necessary path to ensure that these technologies can be safely and effectively integrated into clinical practice. Responsible AI means operating within ethical guardrails. The call for complex explainability in AI equates to enhancing trust and reliability while ensuring that AI-driven decisions are transparent, justifiable, and ultimately beneficial to patient care. As AI will undeniably revolutionize healthcare, the demand for explainability will only grow, making it an imperative area of collaborative focus for researchers, developers, and healthcare providers. They must work to retain extreme complexity and explainability in AI models to ensure robust support in diagnosis, treatment planning, and patient care throughout the entire continuum of care.