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Artificial intelligence (AI) and machine learning (ML) offer significant opportunities in healthcare for innovation with its ability to solve cognitive problems normally requiring human intelligence. However, the potential of ML in healthcare has not been realised to date, with limited existing reports of the clinical and cost benefits that have arisen from their real-world in clinical practice. This is due to the lack of understanding about how some ML models operate and ultimately the way they come to make decisions. Explainable AI (XAI) has emerged as a response to this problem investigating methods and techniques that provide insights into the outcome of an ML model and present it in qualitative understandable terms or visualisations to the stakeholders of the model. This chapter introduces XAI and provides some examples of its use within healthcare.

Machine learning (ML) applications hold significant promise for innovation within healthcare; however, their full potential has not yet been realised, with limited reports of their clinical and cost benefits in clinical practice. This is due to complex clinical, ethical, and legal questions arising from the lack of understanding about how some ML models operate and come to make decisions. eXplainable AI (XAI) is an approach to help address this problem and make ML models understandable. This chapter reports on a systematic literature review investigating the use of XAI in healthcare within the last six years. Three research questions identified as issues in the literature were examined around how bias was dealt with, which XAI techniques were used, and how the applications were evaluated. Findings show that other than class imbalance and missing values, no other types of bias were accounted for in the shortlisted papers. There were no evaluations of the explainability outputs with clinicians and none of the shortlisted papers used an interventional study or RCT.

A Clinical Decision Support System (CDSS) is a technology platform that uses medical knowledge with clinical data to provide customized advice for an individual patient's care. CDSSs use rules to encapsulate expert knowledge and rules engines to infer logic by evaluating rules according to a patient's specific information and related medical facts. However, CDSSs are by nature complex with a plethora of different technologies, standards and methods used to implement them and it can be difficult for practitioners to determine an appropriate solution for a specific scenario. This study's main goal is to provide a better understanding of different technical aspects of a CDSS, identify gaps in CDSS development and ultimately provide some guidelines to assist their translation into practice. We focus on issues related to knowledge representation including use of clinical ontologies, interoperability with EHRs, technology standards, CDSS architecture and mobile/cloud access. This study performs a systematic literature review of rule-based CDSSs that discuss the underlying technologies used and have evaluated clinical outcomes. From a search that yielded an initial set of 1731 papers, only 15 included an evaluation of clinical outcomes. This study has found that a large majority of papers did not include any form of evaluation and, for many that did include an evaluation, the methodology was not sufficiently rigorous to provide statistically significant results. From the 15 papers shortlisted, there were no RCT or quasi-experimental studies, only 6 used ontologies to represent domain knowledge, only 2 integrated with an EHR system, only 5 supported mobile use and only 3 used recognised healthcare technology standards (and all these were HL7 standards). Based on these findings, the paper provides some recommendations for future CDSS development that should be of interest to software developers of CDSSs and also health practitioners involved in commissioning or development.

Assessment is a crucial aspect of any teaching and learning process. New tools such as educational games offer promising advantages: they can personalize feedback to students and save educators time by automating the assessment process. However, while many teachers agree that educational games increase motivation, learning, and retention, few are ready to fully trust them as an assessment tool. A likely reason behind this lack of trust is that educational games are distributed as black boxes, unmodifiable by educators and not providing enough insight about the gameplay. This chapter presents three systematic literature reviews looking into the integration of assessment, feedback, and learning analytics in educational games. It then proposes a framework and present a fully developed engine. The engine is used by both developers and educators. Designed to separate game and assessment, it allows teachers to modify the assessment after distribution and visualize gameplay data via a learning analytics dashboard.