Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation

Background Few studies have investigated the collaborative potential between artificial intelligence (AI) and pulmonologists for diagnosing pulmonary disease. We hypothesised that the collaboration between a pulmonologist and AI with explanations (explainable AI (XAI)) is superior in diagnostic interpretation of pulmonary function tests (PFTs) than the pulmonologist without support. Methods The study was conducted in two phases, a monocentre study (phase 1) and a multicentre intervention study (phase 2). Each phase utilised two different sets of 24 PFT reports of patients with a clinically validated gold standard diagnosis. Each PFT was interpreted without (control) and with XAI's suggestions (intervention). Pulmonologists provided a differential diagnosis consisting of a preferential diagnosis and optionally up to three additional diagnoses. The primary end-point compared accuracy of preferential and additional diagnoses between control and intervention. Secondary end-points were the number of diagnoses in differential diagnosis, diagnostic confidence and inter-rater agreement. We also analysed how XAI influenced pulmonologists’ decisions. Results In phase 1 (n=16 pulmonologists), mean preferential and differential diagnostic accuracy significantly increased by 10.4% and 9.4%, respectively, between control and intervention (p<0.001). Improvements were somewhat lower but highly significant (p<0.0001) in phase 2 (5.4% and 8.7%, respectively; n=62 pulmonologists). In both phases, the number of diagnoses in the differential diagnosis did not reduce, but diagnostic confidence and inter-rater agreement significantly increased during intervention. Pulmonologists updated their decisions with XAI's feedback and consistently improved their baseline performance if AI provided correct predictions. Conclusion A collaboration between a pulmonologist and XAI is better at interpreting PFTs than individual pulmonologists reading without XAI support or XAI alone

Energy governance in Belgium

This chapter reviews the conditions, policies, and institutions of energy governance in Belgium. Except for coal, Belgium has no indigenous energy sources. Nuclear energy accounts for around half of Belgium’s electricity generation but all nuclear power plants are scheduled to phase out by 2025. Energy governance in Belgium is characterized by a lack of a strategic and coherent vision. The responsibilities for energy policy in Belgium are shared among the federal government and the three regions (Flanders, Wallonia, and Brussels). The distribution of competences is very heterogeneous and creates coordination problems. The main drivers of policy initiatives are European directives and international agreements. Belgium is currently not on track to meet its 2020 goals for energy efficiency and emission reductions. A major part of the explanation for Belgium’s weak performance is the dominant role of energy corporations in the Belgian energy sector