Synthesis and biological evaluation of 4H-benzo[e][1,3]oxazin-4-ones analogues of TGX-221 as inhibitors of PI3Kß.

A novel series of TGX-221 analogues was prepared that include isosteric replacement of the 4H-pyrido[1,2-a]pyrimidin-4-one with a 4H-benzo[e][1,3]oxazin-4-one scaffold. The compounds that included an CH(CH3)NH type linker showed comparable activity to TGX-221 analogues with the isosterism supported by the comparative SAR analysis. The analogues containing an CH(CH3)O linker were less active but still showed useful SAR including a favoured o-methyl substitution.

Artificial intelligence explainability: the technical and ethical dimensions.

In recent years, several new technical methods have been developed to make AI-models more transparent and interpretable. These techniques are often referred to collectively as 'AI explainability' or 'XAI' methods. This paper presents an overview of XAI methods, and links them to stakeholder purposes for seeking an explanation. Because the underlying stakeholder purposes are broadly ethical in nature, we see this analysis as a contribution towards bringing together the technical and ethical dimensions of XAI. We emphasize that use of XAI methods must be linked to explanations of human decisions made during the development life cycle. Situated within that wider accountability framework, our analysis may offer a helpful starting point for designers, safety engineers, service providers and regulators who need to make practical judgements about which XAI methods to employ or to require. This article is part of the theme issue 'Towards symbiotic autonomous systems'.

Artificial intelligence in health care: accountability and safety.

The prospect of patient harm caused by the decisions made by an artificial intelligence-based clinical tool is something to which current practices of accountability and safety worldwide have not yet adjusted. We focus on two aspects of clinical artificial intelligence used for decision-making: moral accountability for harm to patients; and safety assurance to protect patients against such harm. Artificial intelligence-based tools are challenging the standard clinical practices of assigning blame and assuring safety. Human clinicians and safety engineers have weaker control over the decisions reached by artificial intelligence systems and less knowledge and understanding of precisely how the artificial intelligence systems reach their decisions. We illustrate this analysis by applying it to an example of an artificial intelligence-based system developed for use in the treatment of sepsis. The paper ends with practical suggestions for ways forward to mitigate these concerns. We argue for a need to include artificial intelligence developers and systems safety engineers in our assessments of moral accountability for patient harm. Meanwhile, none of the actors in the model robustly fulfil the traditional conditions of moral accountability for the decisions of an artificial intelligence system. We should therefore update our conceptions of moral accountability in this context. We also need to move from a static to a dynamic model of assurance, accepting that considerations of safety are not fully resolvable during the design of the artificial intelligence system before the system has been deployed.

La perspective que les décisions prises par un outil clinique basé sur l'intelligence artificielle puissent porter préjudice aux patients est un concept dont les bonnes pratiques de responsabilité et de sécurité actuelles ne tiennent pas encore compte à travers le monde. Nous nous concentrons sur deux aspects qui caractérisent les décisions de l'intelligence artificielle à usage clinique : la responsabilité morale des préjudices aux patients, et la garantie de sécurité pour protéger les patients contre de tels préjudices. Les outils fondés sur l'intelligence artificielle remettent en cause les pratiques cliniques conventionnelles d'attribution des responsabilités et de garantie de la sécurité. Les décisions formulées par les systèmes d'intelligence artificielle sont de moins en moins soumises au contrôle des médecins et spécialistes de la sécurité, qui ne comprennent et ne maîtrisent pas toujours les subtilités régissant cette prise de décision. Nous illustrons notre analyse en l'appliquant à un exemple de système d'intelligence artificielle développé dans le cadre du traitement des infections. Le présent document se termine par une série de suggestions concrètes servant à identifier de nouveaux moyens de tempérer ces inquiétudes. Nous estimons qu'il est nécessaire d'inclure les développeurs à l'origine de l'intelligence artificielle ainsi que les spécialistes de la sécurité des systèmes dans notre évaluation de la responsabilité morale des préjudices causés aux patients. Car pour l'instant, aucun des acteurs impliqués dans le modèle ne remplit pleinement les conditions traditionnelles de responsabilité morale pour les décisions prises par un dispositif d'intelligence artificielle. Dans ce contexte, il est donc essentiel revoir notre conception de la responsabilité morale. Nous devons également passer d'un modèle de garantie statique à un modèle de garantie dynamique, et accepter que certains impératifs de sécurité ne puissent être entièrement résolus durant l'élaboration du système d'intelligence artificielle, avant sa mise en œuvre.

La perspectiva de que los pacientes sufran daños a causa de por las decisiones tomadas por un instrumento clínico de inteligencia artificial es un aspecto al que todavía no se han ajustado las prácticas actuales de responsabilidad y seguridad en todo el mundo. El presente documento se centra en dos aspectos de la inteligencia artificial clínica utilizada para la toma de decisiones: la responsabilidad moral por el daño causado a los pacientes y la garantía de seguridad para proteger a los pacientes contra dicho daño. Las herramientas de inteligencia artificial están desafiando las prácticas clínicas estándar de asignación de responsabilidades y de garantía de seguridad. Los médicos clínicos y los ingenieros de seguridad de las personas tienen menos control sobre las decisiones que adoptan por los sistemas de inteligencia artificial y menos conocimiento y comprensión de la forma precisa en que los sistemas de inteligencia artificial adoptan sus decisiones. Este análisis se ilustra aplicándolo a un ejemplo de un sistema de inteligencia artificial desarrollado para su uso en el tratamiento de la sepsis. El documento termina con sugerencias prácticas sobre las vías de acción para mitigar estas preocupaciones. Se sostiene la necesidad de incluir a los desarrolladores de inteligencia artificial y a los ingenieros de seguridad de sistemas en las evaluaciones de la responsabilidad moral por los daños causados a los pacientes. Entretanto, ninguno de los actores del modelo cumple sólidamente las condiciones tradicionales de responsabilidad moral por las decisiones de un sistema de inteligencia artificial. En consecuencia, se debería actualizar nuestra concepción de la responsabilidad moral en este contexto. También es preciso pasar de un modelo de garantía estático a uno dinámico, aceptando que las consideraciones de seguridad no se pueden resolver plenamente durante el diseño del sistema de inteligencia artificial antes de que el sistema sea implementado.

Preparing to introduce personal health budgets.

A large-scale study ( Forder et al 2012 ) piloting personal health budgets for people with long-term conditions found that they improved patients' quality of life and psychological wellbeing. They were cost-effective and reduced the use of other healthcare services. From April next year, people receiving NHS continuing healthcare funding will have the right to ask for personal health budgets. Some clinical commissioning groups are also introducing them for mental health service users and patients with other long-term conditions. This article outlines the benefits and challenges of introducing personal health budgets, and suggests how nursing managers can begin to consider their role in implementing them.

Baring all: The impact of the hospital gown on patient well-being.

Objectives The importance of personalized and dignified care is increasingly being recognized in health care policy and practice. Despite the known impact of clothing on social identity and self-expression, the impact of hospital clothing on patient well-being has been widely overlooked. Patients are often required to wear hospital clothing, commonly a backless gown, during medical procedures and surgeries. The impact of wearing patient clothing on well-being, during this time of vulnerability, was explored. Design A sequential multi-method approach consisting of two studies. Methods Two studies were carried out to consider the impact of the hospital gown on well-being among adults with and without chronic health conditions. The first study consisted of conducting in-depth, semi-structured interviews (n = 10) with adults living with a lifelong chronic health condition (congenital heart disease). The second study was a cross-sectional online survey exploring adults' views (n = 928) and experiences of wearing the hospital gown. Results Qualitative analysis identified the following master themes: (1) symbolic embodiment of the 'sick' role, (2) relinquishing control to medical professionals, and (3) emotional and physical vulnerability. Quantitative analysis of the online survey data indicated that adults often reported wearing the hospital gown despite a lack of medical necessity. Its design was considered to be not fit for purpose and lacking in dignity. Conclusions The implications of these findings for health policy and practice are discussed, emphasizing the importance of challenging cultural norms in health care since dehumanizing aspects of care, as symbolically represented by the hospital gown, may adversely impact on patient well-being. Statement of contribution What is already known Getting dressed is a form of self-expression, which contributes to the construction of social identity, yet few studies have explored the impact of wearing hospital clothing on patient well-being. The few studies on hospital clothing that exist suggest it is predominantly associated with feeling depersonalized, stigmatized, and devitalized, being in the 'patient role', low status, and a lack of control and privacy. However, previous studies include a variety of hospital clothing including pyjamas (Edvardsson, 2009) and dressing gowns (Topo & Iltanen-Tähkävuori, 2010), whereas in the United Kingdom, a 'one-size-fits-all' backless gown, held together with ties at the back, is most commonly used. What this study adds This study furthers understanding about the lived experience of wearing hospital clothing for people living with a chronic health condition (congenital heart disease) and without. Wearing hospital clothing (most commonly the hospital gown) was associated with symbolic embodiment of the 'sick' role, relinquishing control to medical professionals, and emotional and physical vulnerability for people living with a chronic health condition. Findings from a wider sample, drawn from the general population, suggest that the hospital gown is often being used despite a lack of medical necessity often leaving patients feeling exposed, self-conscious, vulnerable, uncomfortable, cold, embarrassed, and disempowered. These findings are exacerbated for people living with a long-term health condition and women. Together, these studies suggest that the current design of the hospital gown is not fit for purpose and impacts negatively on patient well-being.