What AI Cannot Do for You - The Need for Explainability

In this presentation by a dermatologist from Montreal, the importance of explainable AI (XAI) in medicine is discussed, particularly addressing the limitations of traditional AI systems often regarded as black boxes. The speaker highlights the exponential growth of AI in healthcare, specifically in dermatology, with advancements in computer vision and large language models impacting diagnoses and patient education. However, concerns about AI reliability include issues like hallucinations, where models generate inaccurate information. The lack of transparency in AI decision-making poses challenges for clinicians who need to trust these tools. The concept of XAI aims to make AI more understandable, particularly in high-stakes areas such as medicine, finance, and justice. The presentation distinguishes between interpretability (how a decision is made) and explainability (why it is made) and stresses the need for these qualities to build clinician trust and address biases in AI models. Techniques for achieving explainability include inherent and post hoc methods, with the latter being more common due to the complexity of modern AI models. The benefits of XAI are seen in increasing clinician confidence and optimizing AI performance, but limitations remain, such as the potential for misleading interpretations and a lack of standardized evaluation frameworks. The speaker concludes by posing questions about whether understanding AI processes is necessary given robust performance and the trade-offs between explainability and algorithm efficiency, emphasizing the ongoing relevance of XAI in healthcare.