Explainable AI (XAI): A Balanced Perspective for Healthcare Professionals
Keith Grimes
Fractional Chief Medical / Product / Clinical Safety Officer for HealthTech companies. Specialist in Clinical AI / GenAI
Artificial intelligence (AI) systems, especially deep learning models, have demonstrated impressive capabilities in healthcare, from medical imaging analysis to patient risk prediction (Topol, 2019). However, these complex "black box" systems also have drawbacks, most notably a lack of transparency into how they arrive at conclusions (Holzinger et al., 2017). This has led to increasing interest in explainable AI (XAI) techniques that aim to shed light into the inner workings of AI systems.
XAI refers to methods that provide explanations for AI model decisions and behaviours (Adadi & Berrada, 2018). Proponents argue XAI can increase appropriate trust in AI, reveal potential biases, and enable model improvement (Amann et al., 2020). However, critics point out limitations of current XAI approaches, questioning if they can truly provide meaningful explanations for individual predictions (Ghassemi et al., 2021). This short, AI-generated article summarises key XAI concepts and debates to provide healthcare professionals a balanced perspective on its merits and shortcomings.
Post-hoc explanation methods like saliency maps and Local Interpretable Model-Agnostic Explanations (LIME) are commonly used in medical imaging analysis (Pereira et al., 2018). However, while they highlight influential regions or data points for a decision, they do not necessarily reflect the model's actual reasoning process and can be misleading (Adebayo et al., 2018). Prototype-based explanations that reference canonical features seem more transparent, but still require judgement on if the right features were used appropriately (Chen et al., 2019).
On text or clinical data, highlighting influential words/variables also has interpretability gaps. The model may use shortcut associations between concepts (e.g. gender biases) rather than proper medical logic (Zhang et al., 2020). Moreover, explanations are just approximations of the original complex model. Using them to thoroughly validate individual predictions adds another source of potential error (Ghassemi et al., 2021).
Accordingly, many experts advise caution in using local explanations to justify clinical use of AI systems or elucidate individual decisions (Ghassemi et al., 2021). Rather, rigorous external validation via clinical trials, testing on diverse populations, and ongoing auditing are more reliable ways to ensure safety, efficacy and equity (Obermeyer et al., 2019). Explanations can still be useful for developers and auditors to interrogate models and uncover bugs or biases (Raji et al., 2020).
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Some argue restricting poorly explainable models reinforces medical ethics and patient rights (Wang et al., 2020). However, perfect explainability is not guaranteed even for simple models. Moreover, insisting on explainability could preclude some accurate systems from deployment (Marcus, 2018). A graded, risk-based approach accounting for clinical context may be more appropriate than blanket requirements (Pierson et al., 2021).
In conclusion, while XAI remains an active area of research, current methods have significant limitations regarding local explanations for individual predictions (Ghassemi et al., 2021). For now, healthcare professionals should exercise due diligence before relying on explanations to understand or validate AI system behaviors in high-stakes scenarios. However, ongoing advances in XAI techniques could enable more meaningful applications in the future.
[This article was written using a combination of Claude 2 (Anthropic, 2023) and context from Ghassemi, M et al (2021), Amann, J. et al. (2020), Reddy, S (2022), and my own work. Image is from Ideogram.ai (2023). All references and links have been checked]
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Belgian XR ecosystem
1 年Thanks for sharing Keith!
Noted Author, Speaker & Advocate | Data, AI & Health Literacy for Kids | AI for Good | Role of AI in Medicine | Clinical Innovation & Patient Engagement
1 年While XAI seems like the only way forward when it comes to ethics you make some valid points about the limitations- I think your "wait and see" approach makes sense. I appreciate the transparency on use of AI, I have been doing the same because I think it will bring everyone's fluency forward. I will admit that I gave brief pause when I saw Claude 2 because recently I have experienced more frequently, some pretty significant hallucinations even with simple summaries - which just reinforces your quoted ref about not always being able to explain the behavior even of simple models I think teaching discernment and skepticism is part of the learning curve here!
Topol Fellow in Digital Health | R&D | AI robustness
1 年Seems a decent overview! Relevant references although there are more recent ones.. I’m interested to know how much of the actual content/references was produced by AI and/or how much manual curation was involved?
AI & Emerging Tech in Medicine | L&D | Thought Leader | Educator | Advisor | Consultant | Invited Panelist at G20 Consultation | Featured at Times Square | Quoted in Forbes | LinkedIn's Top Voice
1 年Thanks for sharing your post on explainable AI (XAI) and using AI to summarize your thoughts. I believe transparency on the use of AI is crucial, and it's great to see you mentioning Claude 2 / Ideogram.AI. It helps build trust. Keep up the good work!
Fractional Chief Medical / Product / Clinical Safety Officer for HealthTech companies. Specialist in Clinical AI / GenAI
1 年FAO Anne Marie Cunningham esp!