[Advanced Practice and Digital Health]

Digital health has become indispensable and inseparable from the current healthcare system. However, it is still often presented as a mysterious world that needs to be tamed in order to participate in innovations from their conception and to best accompany patients towards improving the quality and safety of care.

[The effects of the deployment of artificial intelligence on the healthcare professions]

Understanding the effects of the spread of artificial intelligence and robotization on the healthcare professions must be free of prejudice. In this way, it will be possible to promote a real methodology for evaluating and supporting these transformations.

Development of machine learning prediction models to explore nutrients predictive of cardiovascular disease using Canadian linked population-based data

Machine learning may improve use of observational data to understand the nutritional epidemiology of cardiovascular disease (CVD) through better modelling of non-linearity, non-additivity, and dietary complexity. Our objective was to develop machine learning prediction models for exploring how nutrients are related to CVD risk and to evaluate their predictive performance. We established a population-based cohort from the Canadian Community Health Survey and measured CVD incidence and mortality from 2004 to 2018 using administrative databases of national hospital discharges and deaths. Predictors included 61 nutrition variables and fourteen socioeconomic, demographic, psychological, and behavioural variables. Conditional inference forest models were interpreted and evaluated by permutation feature importance, accumulated local effects, and predictive discrimination and calibration. A total of 12 130 individuals were included in the study. Use of supplements, caffeine, and alcohol were the most important nutrition variables for prediction of CVD. Supplement use was associated with decreased risk, caffeine was associated with increasing risk, and alcohol had a u-shaped association with risk. The model had an out-of-sample c-statistic of 0.821 (95% confidence interval = 0.801-0.842). Exploratory findings included both known and novel associations and predictive performance was competitive, suggesting that further application of machine learning to nutritional epidemiology may help elucidate risks and improve predictive models. Novelty: Machine learning prediction models were developed for CVD using dietary data. Models were interpreted with interpretable machine learning techniques, revealing diverse associations between diet and CVD. Models achieved comparable or superior predictive performance to existing CVD risk prediction models.

[From Florence Nightingale to Resuscitation 4.0]

Complex, rough, trying, work and care in intensive care must be rethought and reorganised to reconcile the concern for the well-being of the patient, his relatives and the carers. A configuration where new technologies, the ethical dimension, prevention and training would contribute significantly to the efficiency and humanity of the premises.

Evaluation of the Diet Tracking Smartphone Application Keenoa™: A Qualitative Analysis

Keenoa™ is a novel Canadian diet application (app) currently used by Canadian dietitians to collect diet-related data from clients. The goal of this study was to evaluate Keenoa™ based on user feedback and compare it to a conventional pen and paper method. One hundred and two participants were recruited and randomly assigned to record their diets using this application for 3 nonconsecutive days. Following this, participants were invited to complete an online "exit" survey. Seventy-two subjects responded, with 50 completing an open-ended question asking for general feedback about the app. Data were reviewed and 3 main themes emerged: strengths, challenges, and future recommendations. Strengths associated with the app consisted of picture recognition software, the additional commentary feature, and the overall pleasant data collection process. Challenges that were identified included inconsistencies with the barcode scanning features, the limited food database, time to enter food details, and software issues. Future recommendations included using a larger food database, pairing dietary intake with physical activity monitoring, and having accessible nutritional data. Despite these limitations, participants preferred using mobile apps to record diet compared with traditional written food diaries.

Artificial intelligence in nutrition research: perspectives on current and future applications

Artificial intelligence (AI) is a rapidly evolving area that offers unparalleled opportunities of progress and applications in many healthcare fields. In this review, we provide an overview of the main and latest applications of AI in nutrition research and identify gaps to address to potentialize this emerging field. AI algorithms may help better understand and predict the complex and non-linear interactions between nutrition-related data and health outcomes, particularly when large amounts of data need to be structured and integrated, such as in metabolomics. AI-based approaches, including image recognition, may also improve dietary assessment by maximizing efficiency and addressing systematic and random errors associated with self-reported measurements of dietary intakes. Finally, AI applications can extract, structure and analyze large amounts of data from social media platforms to better understand dietary behaviours and perceptions among the population. In summary, AI-based approaches will likely improve and advance nutrition research as well as help explore new applications. However, further research is needed to identify areas where AI does deliver added value compared with traditional approaches, and other areas where AI is simply not likely to advance the field. Novelty: Artificial intelligence offers unparalleled opportunities of progress and applications in nutrition. There remain gaps to address to potentialize this emerging field.

[Digital healthcare commons: artificial intelligence as a means of inclusion]

The progress made in the medical field thanks to artificial intelligence and its applications has developed doctors' knowledge and patients' knowledge of how to act. By giving a voice to all stakeholders in care, a commons of digital healthcare practices can be formed. Certain therapeutic support and monitoring tools, based on dialogue between health professionals, already exist. By guiding them by means of an approach of collective ethics, they, and likewise artificial intelligence, can help to foster inclusion.

[Positive ethical regulation of digital technology in care plan]

The shift in our healthcare system towards organisational models based on patient care management is one of the structural changes that have taken place in recent years. Digital technology represents a major lever to support this transformation, which has high stakes for improving the quality and efficiency of patient care. Positive regulation of the associated ethical issues can be achieved through the principle of a human guarantee of digital technology and artificial intelligence in health care, which is currently being recognised in the framework of the revision of the bioethics law.

[The human resource: cornerstone of the digital transformation of the healthcare plan]

The e-track, a real challenge for caregivers. We have known for more than twenty years that in order to "defragment" healthcare plan, break down the barriers between the city and the hospital and improve coordination between healthcare professionals, a better flow and sharing of information is essential. We are now talking about a digital shift in the light of accelerating technical progress with the rapid adoption of smartphones and mobile applications, and now the themes of big data and artificial intelligence, which are very present in the media.

[The challenges and contributions of digital technology to improve the performance of the health system]

The digital transformation is already under way in our health system. The deployment of data-driven management and artificial intelligence supports the transition towards treatment methods oriented more towards chronic diseases. Understanding the ethical issues associated with this transformation is a key priority for the future of our health system.

[Artificial intelligence: a benefit for patients?]

While artificial intelligence (AI) may have raised concerns, these questions are now making way for in-depth discussions on how to take advantage of its potential to ensure advances for patients. From this point of view, AI can constitute a real lever for strengthening the doctor-patient relationship, subject to a certain number of conditions.

[Artificial intelligence in healthcare: major potential for innovations in our health system]

Artificial intelligence (AI) is rapidly being extended across health systems with multiple cases of its use already reported. The most operational technique is machine learning with image recognition in imaging. Solutions derived from this approach, as well as other applications of AI, are presented in two major fields: cancer management and geriatric care.

[Artificial intelligence and nursing care: reflections in psychiatry]

A key government priority, artificial intelligence (IA) in healthcare is a real opportunity for nursing professionals. Faced with the daily difficulties encountered, AI could bring a new perspective to nursing care in psychiatry and free up time for professionals which they can then spend with the patient. More training and a multi-disciplinary approach are required.

["The ethical risks associated with artificial intelligence must be identified and regulated"]

Artificial intelligence and its applications in healthcare inevitably raise ethical questions. The 'human guarantee' is at the heart of the discussions. Interview with Cynthia Fleury-Perkins, member of the French national advisory ethics committee and holder of the Humanities and Health Chair of the Conservatoire national des arts et métiers.

[Artificial intelligence in healthcare and the transformation of healthcare professions]

By integrating artificial intelligence (AI) to their practice, healthcare professions will evolve towards more efficient patient management and better quality of care. These changes require new competencies to which all professionals must be trained. A methodology to quantify the impact of AI on healthcare occupations can be used to support and anticipate these changes.

[Healthcare data and artificial intelligence: a geostrategic vision]

The rapid deployment of artificial intelligence (AI) and automation in healthcare is highlighting the importance of health data-driven management as a geostrategic lever. From this point of view, the progress made by the United States and China requires a strong European response to develop a responsible vision which adopts an approach aiming at the positive regulation of AI in healthcare.

[Artificial intelligence, philosophical reflection]

The use of artificial intelligence and robotics in health care means ethical principles need to be established. Artificial and human intelligence must be implemented in such as way as to complement each other. From humanism to anthropotechnics, the definitions of human and humanism are not set in stone. A philosophical reflection can enable their definition to be shaped.

[Artificial intelligence and the nursing profession]

New technologies have revolutionised our society. Artificial intelligence is bringing about radical changes to the healthcare sector.

[Ethics and artificial intelligence in healthcare, towards positive regulation]

The positive regulation of artificial intelligence in healthcare represents a major stake to allow a diffusion of digital innovation, in a spirit of openness and coherence with ethical values. Operational principles have been proposed, particularly around the concept of Human Guarantee. The opinion issued at the end of 2018 by the National Consultative Ethics Committee is an important step forward in the recognition of this idea which leaves a large capacity for initiative to professionals and patients.