Explainability in medicine in an era of AI-based clinical decision support systems

This is the final version. Available on open access from Frontiers Media via the DOI in this recordData availability statement: The original contributions presented in the study are included in the article/supplementary material; further inquiries can be directed to the corresponding author.The combination of "Big Data" and Artificial Intelligence (AI) is frequently promoted as having the potential to deliver valuable health benefits when applied to medical decision-making. However, the responsible adoption of AI-based clinical decision support systems faces several challenges at both the individual and societal level. One of the features that has given rise to particular concern is the issue of explainability, since, if the way an algorithm arrived at a particular output is not known (or knowable) to a physician, this may lead to multiple challenges, including an inability to evaluate the merits of the output. This "opacity" problem has led to questions about whether physicians are justified in relying on the algorithmic output, with some scholars insisting on the centrality of explainability, while others see no reason to require of AI that which is not required of physicians. We consider that there is merit in both views but find that greater nuance is necessary in order to elucidate the underlying function of explainability in clinical practice and, therefore, its relevance in the context of AI for clinical use. In this paper, we explore explainability by examining what it requires in clinical medicine and draw a distinction between the function of explainability for the current patient versus the future patient. This distinction has implications for what explainability requires in the short and long term. We highlight the role of transparency in explainability, and identify semantic transparency as fundamental to the issue of explainability itself. We argue that, in day-to-day clinical practice, accuracy is sufficient as an "epistemic warrant" for clinical decision-making, and that the most compelling reason for requiring explainability in the sense of scientific or causal explanation is the potential for improving future care by building a more robust model of the world. We identify the goal of clinical decision-making as being to deliver the best possible outcome as often as possible, and find-that accuracy is sufficient justification for intervention for today's patient, as long as efforts to uncover scientific explanations continue to improve healthcare for future patients.Research Foundation Flanders (FWO

Requirements for physician competencies in allergy: key clinical competencies appropriate for the care of patients with allergic or immunologic diseases

Egypt J Pediatr Allergy Immunol 2009; 7(1): 33-3

Phenotypic and genotypic analyses to guide selection of reverse transcriptase inhibitors in second-line HIV therapy following extended virological failure in Uganda

Objectives We investigated phenotypic and genotypic resistance after 2 years of first-line therapy with two HIV treatment regimens in the absence of virological monitoring. Methods NORA [Nevirapine OR Abacavir study, a sub-study of the Development of AntiRetroviral Therapy in Africa (DART) trial] randomized 600 symptomatic HIV-infected Ugandan adults (CD4 cell count <200 cells/mm3) to receive zidovudine/lamivudine plus abacavir (cABC arm) or nevirapine (cNVP arm). All virological tests were performed retrospectively, including resistance tests on week 96 plasma samples with HIV RNA levels ≥1000 copies/mL. Phenotypic resistance was expressed as fold-change in IC50 (FC) relative to wild-type virus. Results HIV-1 RNA viral load ≥1000 copies/mL at week 96 was seen in 58/204 (28.4%) cABC participants and 21/159 (13.2%) cNVP participants. Resistance results were available in 35 cABC and 17 cNVP participants; 31 (89%) cABC and 16 (94%) cNVP isolates had a week 96 FC below the biological cut-off for tenofovir (2.2). In the cNVP arm, 16/17 participants had resistance mutations synonymous with high-level resistance to nevirapine and efavirenz; FC values for etravirine were above the biological cut-off in 9 (53%) isolates. In multivariate regression models, K65R, Y115F and the presence of thymidine analogue-associated mutations were associated with increased susceptibility to etravirine in the cABC arm. Conclusions Our data support the use of tenofovir following failure of a first-line zidovudine-containing regimen and shed further light on non-nucleoside reverse transcriptase inhibitor hypersusceptibility

Exploratory factor analysis of self-reported symptoms in a large, population-based military cohort

<p>Abstract</p> <p>Background</p> <p>US military engagements have consistently raised concern over the array of health outcomes experienced by service members postdeployment. Exploratory factor analysis has been used in studies of 1991 Gulf War-related illnesses, and may increase understanding of symptoms and health outcomes associated with current military conflicts in Iraq and Afghanistan. The objective of this study was to use exploratory factor analysis to describe the correlations among numerous physical and psychological symptoms in terms of a smaller number of unobserved variables or factors.</p> <p>Methods</p> <p>The Millennium Cohort Study collects extensive self-reported health data from a large, population-based military cohort, providing a unique opportunity to investigate the interrelationships of numerous physical and psychological symptoms among US military personnel. This study used data from the Millennium Cohort Study, a large, population-based military cohort. Exploratory factor analysis was used to examine the covariance structure of symptoms reported by approximately 50,000 cohort members during 2004-2006. Analyses incorporated 89 symptoms, including responses to several validated instruments embedded in the questionnaire. Techniques accommodated the categorical and sometimes incomplete nature of the survey data.</p> <p>Results</p> <p>A 14-factor model accounted for 60 percent of the total variance in symptoms data and included factors related to several physical, psychological, and behavioral constructs. A notable finding was that many factors appeared to load in accordance with symptom co-location within the survey instrument, highlighting the difficulty in disassociating the effects of question content, location, and response format on factor structure.</p> <p>Conclusions</p> <p>This study demonstrates the potential strengths and weaknesses of exploratory factor analysis to heighten understanding of the complex associations among symptoms. Further research is needed to investigate the relationship between factor analytic results and survey structure, as well as to assess the relationship between factor scores and key exposure variables.</p

The ineffectiveness of entrepreneurship policy:Is policy formulation to blame?

Entrepreneurship policy has been criticised for its lack of effectiveness. Some scholars, such as Scott Shane in this journal, have argued that it is ‘bad’ public policy. But this simply begs the question why the legislative process should generate bad policy? To answer this question this study examines the UK’s enterprise policy process in the 2009–2010 period. It suggests that a key factor for the ineffectiveness of policy is how it is formulated. This stage in the policy process is seldom visible to those outside of government departments and has been largely ignored by prior research. The application of institutional theory provides a detailed theoretical understanding of the actors and the process by which enterprise policy is formulated. We find that by opening up the ‘black box’ of enterprise policy formulation, the process is dominated by powerful actors who govern the process with their interests

UK prescribing practices as proxy markers of unmet need in allergic rhinitis:A retrospective observational study

Little data on UK prescribing patterns and treatment effectiveness for allergic rhinitis (AR) are available. We quantified unmet pharmacologic needs in AR by assessing AR treatment effectiveness based on the prescribing behaviour of UK general practitioners (GP) during two consecutive pollen seasons (2009 and 2010). We conducted a retrospective observational study with the data from the Optimum Patient Care Research Database. We assessed diagnoses and prescription data for patients with a recorded diagnosis of rhinitis who took rhinitis medication during the study period. We assessed the data from 25,069 patients in 2009 and 22,381 patients in 2010. Monotherapy was the initial prescription of the season for 67% of patients with seasonal AR (SAR) and 77% of patients with nonseasonal upper airways disease (NSUAD), for both years. Initial oral antihistamine (OAH) or intranasal corticosteroid (INS) monotherapy proved insufficient for 420% of SAR and 437% of NSUAD patients. Multiple therapy was the initial prescription for 33% of SAR and 23% of NSUAD in both years, rising to 45% and 450% by season end, respectively. For NSUAD, dual-therapy prescriptions doubled and triple-therapy prescriptions almost tripled during both seasons. Many patients revisited their GP regardless of initial prescription. Initial OAH or INS monotherapy provides insufficient symptom control for many AR patients. GPs often prescribe multiple therapies at the start of the season, with co-prescription becoming more common as the season progresses. However, patients prescribed multiple therapies frequently revisit their GP, presumably to adjust treatment. These data suggest the need for more effective AR treatment and management strategies