Citizens and Citizenship The Rhetoric of Dutch Immigrant Integration Policy in 2011

The past generation has seen a switch to restrictive policies and language in the governance of migrants living in the Netherlands. Beginning in 2010, a new government with right-wing populist backing went further, declaring the centrality of proposed characteristic historic Dutch values. In this article, we investigate a key policy document to characterize and understand this policy change. Discourse analysis as an exploration of language choices, including use of ideas from rhetoric, helps us apply and test ideas from governmentality studies of migration and from discourse studies as social theorizing. We trace the chosen problem formulation; the delineation, naming, and predication of population categories; the understanding of citizenship, community, and integration; and the overall rhetoric, including chosen metaphors and nuancing of emphases, that links the elements into a meaning-rich world picture. A “neoliberal communitarian” conception of citizenship has emerged that could unfortunately subject many immigrants to marginalization and exclusion

Trust as Glue in Nanotechnology Governance Networks

This paper reflects on the change of relations among participants in nanotechnology governance through their participation in governance processes such as stakeholder dialogues. I show that policymaking in practice—that is, the practice of coming and working together in such stakeholder dialogues—has the potential for two-fold performative effects: it can contribute to the development of trust and mutual responsibility on the part of the involved actors, and it may bring about effects on the formation of boundaries of what is sayable and thinkable in nanotechnology governance. Three vignettes about the work of the German NanoKommission indicate the development of new relations of trust, recognition and mutual responsibility among actors. It is concluded that governance in practice can assemble new collectives in which relations of trust are the glue holding the complex structure together. While such a consensus-based progress may be favourable for smooth technology development, it can be considered problematic if evaluated against the ideals of deliberative democracy, which often form the premises on which public engagement is based. Stakeholder forums were set in place with the intention of including various actors, but this is Janus-faced: if a dialogue becomes encapsulated in new governance networks, new exclusions can arise. For example, a policing of which information is released to a wider audience can occur

International Stem Cell Collaboration: How Disparate Policies between the United States and the United Kingdom Impact Research

As the scientific community globalizes, it is increasingly important to understand the effects of international collaboration on the quality and quantity of research produced. While it is generally assumed that international collaboration enhances the quality of research, this phenomenon is not well examined. Stem cell research is unique in that it is both politically charged and a research area that often generates international collaborations, making it an ideal case through which to examine international collaborations. Furthermore, with promising medical applications, the research area is dynamic and responsive to a globalizing science environment. Thus, studying international collaborations in stem cell research elucidates the role of existing international networks in promoting quality research, as well as the effects that disparate national policies might have on research. This study examined the impact of collaboration on publication significance in the United States and the United Kingdom, world leaders in stem cell research with disparate policies. We reviewed publications by US and UK authors from 2008, along with their citation rates and the political factors that may have contributed to the number of international collaborations. The data demonstrated that international collaborations significantly increased an article's impact for UK and US investigators. While this applied to UK authors whether they were corresponding or secondary, this effect was most significant for US authors who were corresponding authors. While the UK exhibited a higher proportion of international publications than the US, this difference was consistent with overall trends in international scientific collaboration. The findings suggested that national stem cell policy differences and regulatory mechanisms driving international stem cell research in the US and UK did not affect the frequency of international collaborations, or even the countries with which the US and UK most often collaborated. Geographical and traditional collaborative relationships were the predominate considerations in establishing international collaborations

Towards Conversational Diagnostic AI

At the heart of medicine lies the physician-patient dialogue, where skillful history-taking paves the way for accurate diagnosis, effective management, and enduring trust. Artificial Intelligence (AI) systems capable of diagnostic dialogue could increase accessibility, consistency, and quality of care. However, approximating clinicians' expertise is an outstanding grand challenge. Here, we introduce AMIE (Articulate Medical Intelligence Explorer), a Large Language Model (LLM) based AI system optimized for diagnostic dialogue. AMIE uses a novel self-play based simulated environment with automated feedback mechanisms for scaling learning across diverse disease conditions, specialties, and contexts. We designed a framework for evaluating clinically-meaningful axes of performance including history-taking, diagnostic accuracy, management reasoning, communication skills, and empathy. We compared AMIE's performance to that of primary care physicians (PCPs) in a randomized, double-blind crossover study of text-based consultations with validated patient actors in the style of an Objective Structured Clinical Examination (OSCE). The study included 149 case scenarios from clinical providers in Canada, the UK, and India, 20 PCPs for comparison with AMIE, and evaluations by specialist physicians and patient actors. AMIE demonstrated greater diagnostic accuracy and superior performance on 28 of 32 axes according to specialist physicians and 24 of 26 axes according to patient actors. Our research has several limitations and should be interpreted with appropriate caution. Clinicians were limited to unfamiliar synchronous text-chat which permits large-scale LLM-patient interactions but is not representative of usual clinical practice. While further research is required before AMIE could be translated to real-world settings, the results represent a milestone towards conversational diagnostic AI.Comment: 46 pages, 5 figures in main text, 19 figures in appendi

Towards Accurate Differential Diagnosis with Large Language Models

An accurate differential diagnosis (DDx) is a cornerstone of medical care, often reached through an iterative process of interpretation that combines clinical history, physical examination, investigations and procedures. Interactive interfaces powered by Large Language Models (LLMs) present new opportunities to both assist and automate aspects of this process. In this study, we introduce an LLM optimized for diagnostic reasoning, and evaluate its ability to generate a DDx alone or as an aid to clinicians. 20 clinicians evaluated 302 challenging, real-world medical cases sourced from the New England Journal of Medicine (NEJM) case reports. Each case report was read by two clinicians, who were randomized to one of two assistive conditions: either assistance from search engines and standard medical resources, or LLM assistance in addition to these tools. All clinicians provided a baseline, unassisted DDx prior to using the respective assistive tools. Our LLM for DDx exhibited standalone performance that exceeded that of unassisted clinicians (top-10 accuracy 59.1% vs 33.6%, [p = 0.04]). Comparing the two assisted study arms, the DDx quality score was higher for clinicians assisted by our LLM (top-10 accuracy 51.7%) compared to clinicians without its assistance (36.1%) (McNemar's Test: 45.7, p < 0.01) and clinicians with search (44.4%) (4.75, p = 0.03). Further, clinicians assisted by our LLM arrived at more comprehensive differential lists than those without its assistance. Our study suggests that our LLM for DDx has potential to improve clinicians' diagnostic reasoning and accuracy in challenging cases, meriting further real-world evaluation for its ability to empower physicians and widen patients' access to specialist-level expertise