Using Explainable Machine Learning to Explore the Impact of Synoptic Reporting on Prostate Cancer

Identity of AI

With the explosion of Artificial Intelligence (AI) as an area of study and practice, it has gradually become very difficult to mark its boundaries precisely and specify what exactly it encompasses. Many other areas of study are interwoven with AI, and new research and development topics that require interdisciplinary approach frequently attract attention. In addition, several AI subfields and topics are home to long-time controversies that give rise to seemingly never-ending debates that further obfuscate the entire area of AI and make its boundaries even more indistinct. To tackle such problems in a systematic way, this paper introduces the concept of identity of AI (viewed as an area of study) and discusses its dynamics, controversies, contradictions, and opposing opinions and approaches, coming from different sources and stakeholders. The concept of identity of AI emerges as a set of characteristics that shape up the current outlook on AI from epistemological, philosophical, ethical, technological, and social perspectives.

Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review

Background and Objective

Machine learning (ML) models are increasingly being utilized in oncology research for use in the clinic. However, while more complicated models may provide improvements in predictive or prognostic power, a hurdle to their adoption are limits of model interpretability, wherein the inner workings can be perceived as a "black box". Explainable artificial intelligence (XAI) frameworks including Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) are novel, model-agnostic approaches that aim to provide insight into the inner workings of the "black box" by producing quantitative visualizations of how model predictions are calculated. In doing so, XAI can transform complicated ML models into easily understandable charts and interpretable sets of rules, which can give providers with an intuitive understanding of the knowledge generated, thus facilitating the deployment of such models in routine clinical workflows.

Methods

We performed a comprehensive, non-systematic review of the latest literature to define use cases of model-agnostic XAI frameworks in oncologic research. The examined database was PubMed/MEDLINE. The last search was run on May 1, 2022.

Key Content and Findings

In this review, we identified several fields in oncology research where ML models and XAI were utilized to improve interpretability, including prognostication, diagnosis, radiomics, pathology, treatment selection, radiation treatment workflows, and epidemiology. Within these fields, XAI facilitates determination of feature importance in the overall model, visualization of relationships and/or interactions, evaluation of how individual predictions are produced, feature selection, identification of prognostic and/or predictive thresholds, and overall confidence in the models, among other benefits. These examples provide a basis for future work to expand on, which can facilitate adoption in the clinic when the complexity of such modeling would otherwise be prohibitive.

Conclusions

Model-agnostic XAI frameworks offer an intuitive and effective means of describing oncology ML models, with applications including prognostication and determination of optimal treatment regimens. Using such frameworks presents an opportunity to improve understanding of ML models, which is a critical step to their adoption in the clinic.