Interpretable AI in Healthcare: Enhancing Fairness, Safety, and Trust

MacDonald, Samual; Steven, Kaiah; Trzaskowski, Maciej

doi:10.1007/978-981-19-1223-8_11

Cited by 12 publications

(7 citation statements)

References 16 publications

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“…Integrating an AI system into a health care system is a multistep process that requires careful planning and execution. This process includes setting up the necessary infrastructure and technology to support the AI system, connecting it to other existing health technologies, training health care professionals on how to use and maintain the AI system, regularly monitoring its performance and making adjustments as needed, and ensuring compliance with all relevant regulations and guidelines for the use of AI in health care [58][59][60][61][62]. However, the implementation of these processes is not always straightforward.…”

Section: Model Deployment Operationalization Monitoring and Maintenancementioning

confidence: 99%

Human-Centered Design to Address Biases in Artificial Intelligence

Chen¹,

Clayton²,

Novak³

et al. 2023

J Med Internet Res

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The potential of artificial intelligence (AI) to reduce health care disparities and inequities is recognized, but it can also exacerbate these issues if not implemented in an equitable manner. This perspective identifies potential biases in each stage of the AI life cycle, including data collection, annotation, machine learning model development, evaluation, deployment, operationalization, monitoring, and feedback integration. To mitigate these biases, we suggest involving a diverse group of stakeholders, using human-centered AI principles. Human-centered AI can help ensure that AI systems are designed and used in a way that benefits patients and society, which can reduce health disparities and inequities. By recognizing and addressing biases at each stage of the AI life cycle, AI can achieve its potential in health care.

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Section: Model Deployment Operationalization Monitoring and Maintenancementioning

confidence: 99%

Human-Centered Design to Address Biases in Artificial Intelligence

Chen¹,

Clayton²,

Novak³

et al. 2023

J Med Internet Res

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“…As medical professionals increasingly rely on AI-powered systems to aid in diagnosis and treatment planning [18], the need for interpretability and transparency in AI models becomes paramount [19]. Deep learning models, including ViTs, often exhibit highly complex and intricate internal representations, making it challenging for experts to comprehend their decision-making process.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care

Lai

2024

BioMedInformatics

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Recent advancements in artificial intelligence (AI) have facilitated its widespread adoption in primary medical services, addressing the demand–supply imbalance in healthcare. Vision Transformers (ViT) have emerged as state-of-the-art computer vision models, benefiting from self-attention modules. However, compared to traditional machine learning approaches, deep learning models are complex and are often treated as a “black box” that can cause uncertainty regarding how they operate. Explainable artificial intelligence (XAI) refers to methods that explain and interpret machine learning models’ inner workings and how they come to decisions, which is especially important in the medical domain to guide healthcare decision-making processes. This review summarizes recent ViT advancements and interpretative approaches to understanding the decision-making process of ViT, enabling transparency in medical diagnosis applications.

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“…In general, studies using DL show excellent predictive performance, providing hope for successful translation into clinical practice 13 , 14 . However, prediction accuracy in DL comes with potential pitfalls which need to be overcome before wider adoption can be eventuated 15 .…”

Section: Introductionmentioning

confidence: 99%

Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology

MacDonald

Foley²,

Yap³

et al. 2023

Sci Rep

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Uncertainty estimation is crucial for understanding the reliability of deep learning (DL) predictions, and critical for deploying DL in the clinic. Differences between training and production datasets can lead to incorrect predictions with underestimated uncertainty. To investigate this pitfall, we benchmarked one pointwise and three approximate Bayesian DL models for predicting cancer of unknown primary, using three RNA-seq datasets with 10,968 samples across 57 cancer types. Our results highlight that simple and scalable Bayesian DL significantly improves the generalisation of uncertainty estimation. Moreover, we designed a prototypical metric—the area between development and production curve (ADP), which evaluates the accuracy loss when deploying models from development to production. Using ADP, we demonstrate that Bayesian DL improves accuracy under data distributional shifts when utilising ‘uncertainty thresholding’. In summary, Bayesian DL is a promising approach for generalising uncertainty, improving performance, transparency, and safety of DL models for deployment in the real world.

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Interpretable AI in Healthcare: Enhancing Fairness, Safety, and Trust

Cited by 12 publications

References 16 publications

Human-Centered Design to Address Biases in Artificial Intelligence

Human-Centered Design to Address Biases in Artificial Intelligence

Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care

Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology

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