Literatura científica selecionada sobre o tema "Intelligence artificielle – Santé"
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Artigos de revistas sobre o assunto "Intelligence artificielle – Santé"
Portnoff, André-Yves. "Santé et intelligence artificielle". Futuribles N° 425, n.º 4 (2018): 53. http://dx.doi.org/10.3917/futur.425.0053.
Texto completo da fonteEZANNO, Pauline, Sébastien PICAULT, Nathalie WINTER, Gaël BEAUNÉE, Hervé MONOD e Jean-François GUÉGAN. "Intelligence artificielle et santé animale". INRAE Productions Animales 33, n.º 2 (15 de setembro de 2020): 95–108. http://dx.doi.org/10.20870/productions-animales.2020.33.2.3572.
Texto completo da fonteMaisnier-Boché, Lorraine. "Intelligence artificielle et données de santé". Journal du Droit de la Santé et de l’Assurance - Maladie (JDSAM) N° 17, n.º 3 (1 de julho de 2017): 25–29. http://dx.doi.org/10.3917/jdsam.173.0025.
Texto completo da fonteMatuchansky, Claude. "Intelligence clinique et intelligence artificielle". médecine/sciences 35, n.º 10 (outubro de 2019): 797–803. http://dx.doi.org/10.1051/medsci/2019158.
Texto completo da fonteDrahi, Éric, Yves Le Noc, Gérard Bergua, Marc Dumoulin, Dragos-Paul Hagiu, Claude Scali e Élisabeth Steyer. "Intelligence artificielle en santé : principes, performances et éthique". Médecine 20, n.º 10 (1 de dezembro de 2024): 208–12. http://dx.doi.org/10.1684/med.2024.994.
Texto completo da fonteParmentier, Florent. "Données de santé et intelligence artificielle : une vision géostratégique". Soins 64, n.º 838 (setembro de 2019): 53–55. http://dx.doi.org/10.1016/j.soin.2019.06.013.
Texto completo da fonteBranellec, Gurvan, e Slim Hadoussa. "Intelligence artificielle et santé, enjeux managériaux, juridiques et éthiques". Soins Cadres 29, n.º 123 (novembro de 2020): 33–36. http://dx.doi.org/10.1016/j.scad.2020.10.010.
Texto completo da fonteMorlet-Haïdara, Lydia. "Table-ronde 4 : Innovation en santé / Numérique / Intelligence Artificielle". Journal du Droit de la Santé et de l’Assurance - Maladie (JDSAM) N° 33, n.º 3 (1 de julho de 2022): 51–57. http://dx.doi.org/10.3917/jdsam.223.0051.
Texto completo da fontePon, Dominique. "Table-ronde 4 : Innovation en santé / Numérique / Intelligence Artificielle". Journal du Droit de la Santé et de l’Assurance - Maladie (JDSAM) N° 33, n.º 3 (1 de julho de 2022): 64–65. http://dx.doi.org/10.3917/jdsam.223.0064.
Texto completo da fonteLucas, Jacques. "Table-ronde 4 : Innovation en santé / Numérique / Intelligence Artificielle". Journal du Droit de la Santé et de l’Assurance - Maladie (JDSAM) N° 33, n.º 3 (1 de julho de 2022): 61–63. http://dx.doi.org/10.3917/jdsam.223.0061.
Texto completo da fonteTeses / dissertações sobre o assunto "Intelligence artificielle – Santé"
Clampitt, Megan. "Indexation de l'état de santé des coraux par une approche basée sur l'intelligence artificielle". Electronic Thesis or Diss., Université Côte d'Azur, 2023. http://www.theses.fr/2023COAZ6019.
Texto completo da fonteCoral reefs are deteriorating at a startling rate and the development of fast and efficient monitoring schemas that attempt to evaluate coral health without only focusing on the absence or presence of disease or bleaching is essential. My Ph.D. research aims to combine the fields of Coral Biology, Computer Science, and Marine Conservation with the main question of my thesis being: how can artificial intelligence tools be used to assess coral health states from colony photographs? Since the assessment of individual coral colony health state remains poorly defined, our approach is to use AI tools to assess visual cues such as physically damaging conditions (boring organisms & predation), contact with other organisms (algae, sediment), and color changes that could correlate with health states. This was achieved by utilizing photographic data from the Tara Pacific Expedition to build the first version of AI machines capable of automatically recognizing these visual cues and then applying this tool to two types of field studies i). A longitudinal study set up in Moorea, French Polynesia aimed to investigate coral health as assessed by mortality/partial mortality events. ii). A comparative study between damaged, pristine, and restoration sites in Raja Ampat, Indonesia. The objective of these studies is to extract the visual cues that distinguish healthy from unhealthy corals. Thus, I was able to create an AI Model capable of automatically annotating coral colony photographs for visual cues relevant to the current health state of the colony
Ouédraogo, Ismaila. "Technologie mobile et intelligence artificielle pour l'amélioration de la littératie en santé dans les milieux défavorisés". Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0023.
Texto completo da fonteAccess and use of health information is indeed a major challenge in sub-Saharan Africa, especially for populations with low literacy. These difficulties are exacerbated by the increasing prevalence of foreign language content in digital health solutions, as well as the sometimes inadequate design of these solutions for local populations. These factors must be taken into account in the development and implementation of digital health solutions to ensure that they are truly accessible and beneficial to all populations. In this context, this research focuses on improving the accessibility and use of health information (health literacy) among lowliterate populations in Burkina Faso through AI-enabled mobile health solutions. The research methodology includes literature reviews, interviews, surveys and observations to accurately understand the specific needs of low literacy users. Based on this feedback, concrete design principles will be established to guide the development of a prototype Interactive Voice Response (IVR) system in the Dioula language. The mobile service is then evaluated with users to enable iterative improvements to the solution, taking user feedback into account. In addition, this research contributes to the creation of annotated speech data in Dioula to address the lack of speech data for assistive speech technologies for the population. By highlighting the importance of local languages and adapted technologies, this study demonstrates how AI-enabled mobile health solutions can effectively overcome barriers related to literacy to improve the health literacy of marginalised populations. The findings of this study are in line with the United Nations Sustainable Development Goals (SDGs), thus reinforcing their positive impact on the health of vulnerable populations in Burkina Faso
Mercadier, Yves. "Classification automatique de textes par réseaux de neurones profonds : application au domaine de la santé". Thesis, Montpellier, 2020. http://www.theses.fr/2020MONTS068.
Texto completo da fonteThis Ph.D focuses on the analysis of textual data in the health domain and in particular on the supervised multi-class classification of data from biomedical literature and social media.One of the major difficulties when exploring such data by supervised learning methods is to have a sufficient number of data sets for models training. Indeed, it is generally necessary to label manually the data before performing the learning step. The large size of the data sets makes this labellisation task very expensive, which should be reduced with semi-automatic systems.In this context, active learning, in which the Oracle intervenes to choose the best examples to label, is promising. The intuition is as follows: by choosing the smartly the examples and not randomly, the models should improve with less effort for the oracle and therefore at lower cost (i.e. with less annotated examples). In this PhD, we will evaluate different active learning approaches combined with recent deep learning models.In addition, when small annotated data set is available, one possibility of improvement is to artificially increase the data quantity during the training phase, by automatically creating new data from existing data. More precisely, we inject knowledge by taking into account the invariant properties of the data with respect to certain transformations. The augmented data can thus cover an unexplored input space, avoid overfitting and improve the generalization of the model. In this Ph.D, we will propose and evaluate a new approach for textual data augmentation.These two contributions will be evaluated on different textual datasets in the medical domain
Yameogo, Relwende Aristide. "Risques et perspectives du big data et de l'intelligence artificielle : approche éthique et épistémologique". Thesis, Normandie, 2020. http://www.theses.fr/2020NORMLH10.
Texto completo da fonteIn the 21st century, the use of big data and AI in the field of health has gradually expanded, although it is accompanied by problems linked to the emergence of practices based on the use of digital traces. The aim of this thesis is to evaluate the use of big data and AI in medical practice, to discover the processes generated by digital tools in the field of health and to highlight the ethical problems they pose.The use of ICTs in medical practice is mainly based on the use of EHR, prescription software and connected objects. These uses raise many problems for physicians who are aware of the risk involved in protecting patients' health data. In this work, we are implementing a method for designing CDSS, the temporal fuzzy vector space. This method allows us to model a new clinical diagnostic score for pulmonary embolism. Through the "Human-trace" paradigm, our research allows us not only to measure the limitation in the use of ICT, but also to highlight the interpretative biases due to the delinking between the individual caught in his complexity as a "Human-trace" and the data circulating about him via digital traces. If big data, coupled with AI can play a major role in the implementation of CDSS, it cannot be limited to this field. We are also studying how to set up big data and AI development processes that respect the deontological and medical ethics rules associated with the appropriation of ICTs by the actors of the health system
La, Barbera Giammarco. "Learning anatomical digital twins in pediatric 3D imaging for renal cancer surgery". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT040.
Texto completo da fontePediatric renal cancers account for 9% of pediatric cancers with a 9/10 survival rate at the expense of the loss of a kidney. Nephron-sparing surgery (NSS, partial removal of the kidney) is possible if the cancer meets specific criteria (regarding volume, location and extent of the lesion). Indication for NSS is relying on preoperative imaging, in particular X-ray Computerized Tomography (CT). While assessing all criteria in 2D images is not always easy nor even feasible, 3D patient-specific models offer a promising solution. Building 3D models of the renal tumor anatomy based on segmentation is widely developed in adults but not in children. There is a need of dedicated image processing methods for pediatric patients due to the specificities of the images with respect to adults and to heterogeneity in pose and size of the structures (subjects going from few days of age to 16 years). Moreover, in CT images, injection of contrast agent (contrast-enhanced CT, ceCT) is often used to facilitate the identification of the interface between different tissues and structures but this might lead to heterogeneity in contrast and brightness of some anatomical structures, even among patients of the same medical database (i.e., same acquisition procedure). This can complicate the following analyses, such as segmentation. The first objective of this thesis is to perform organ/tumor segmentation from abdominal-visceral ceCT images. An individual 3D patient model is then derived. Transfer learning approaches (from adult data to children images) are proposed to improve state-of-the-art performances. The first question we want to answer is if such methods are feasible, despite the obvious structural difference between the datasets, thanks to geometric domain adaptation. A second question is if the standard techniques of data augmentation can be replaced by data homogenization techniques using Spatial Transformer Networks (STN), improving training time, memory requirement and performances. In order to deal with variability in contrast medium diffusion, a second objective is to perform a cross-domain CT image translation from ceCT to contrast-free CT (CT) and vice-versa, using Cycle Generative Adversarial Network (CycleGAN). In fact, the combined use of ceCT and CT images can improve the segmentation performances on certain anatomical structures in ceCT, but at the cost of a double radiation exposure. To limit the radiation dose, generative models could be used to synthesize one modality, instead of acquiring it. We present an extension of CycleGAN to generate such images, from unpaired databases. Anatomical constraints are introduced by automatically selecting the region of interest and by using the score of a Self-Supervised Body Regressor, improving the selection of anatomically-paired images between the two domains (CT and ceCT) and enforcing anatomical consistency. A third objective of this work is to complete the 3D model of patient affected by renal tumor including also arteries, veins and collecting system (i.e. ureters). An extensive study and benchmarking of the literature on anatomic tubular structure segmentation is presented. Modifications to state-of-the-art methods for our specific application are also proposed. Moreover, we present for the first time the use of the so-called vesselness function as loss function for training a segmentation network. We demonstrate that combining eigenvalue information with structural and voxel-wise information of other loss functions results in an improvement in performance. Eventually, a tool developed for using the proposed methods in a real clinical setting is shown as well as a clinical study to further evaluate the benefits of using 3D models in pre-operative planning. The intent of this research is to demonstrate through a retrospective evaluation of experts how criteria for NSS are more likely to be found in 3D compared to 2D images. This study is still ongoing
Hadidi, Tareq. "Modélisation et simulation des déplacements de la vie quotidienne dans un habitat intelligent pour la santé". Thesis, Grenoble, 2011. http://www.theses.fr/2011GRENS005.
Texto completo da fonteOur societies will have to meet rapidly the needs of 14 million people who often live in situations of loss of autonomy or dependence with quick solutions supported. Telemedicine, and especially the home telemonitoring, is now a solution to alleviate the shortage of health professionals confronted to the great increase in population in Europe. In this context, we investigated the HIS "Smart Habitat for Health". The work of this thesis was to develop a digital simulator of activities (displacements) of a person followed within a HIS. The creation of a simulator is seen as a solution to improve performance and quality of service of home telemonitoring. We tested several methods of simulation (artificial neural networks, Markov chains, Polya urns) and retained the hidden Markov (HMM). This simulator was implemented under MATLAB, after the modeling of data collected in HIS occupied by elderly people, some living alone. Validation of data generated by the simulator was performed by measuring surface correlation between real and simulated data. This work paves the way for production activity data simulated according to a profile type of patient, without going through lengthy and costly field experiments
Curé, Olivier. "Siam : système intéractif d'automédication multimédia". Paris 5, 1999. http://www.theses.fr/1999PA05S019.
Texto completo da fonteDi, Marco Lionel. "Récit d'ingénierie pédagogique en santé à l'usage de l'enseignant connecté Does the acceptance of hybrid learning affect learning approaches in France? Blended Learning for French Health Students: Does Acceptance of a Learning Management System Influence Students’ Self-Efficacy?" Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALS024.
Texto completo da fonteBackground. The general objective of this thesis was to evaluate a hybrid pedagogical method using an integrated learning environment (ILE) in the training of health professionals. Three research questions followed one after the other. Does the acceptability of blended learning affect students' learning strategies and learning approaches? Does the acceptability of an ILE affect students' self-efficacy? What characteristics of a dematerialised course make students' attention variable?Materials & Methods. We carried out 2 quantitative observational studies, as well as a single-blind observational experiment coupled with a qualitative analysis, with different classes of midwifery students of Grenoble-Alpes University Faculty of Medicine.Results. Students have suited learning approaches and strategies despite the use of a hybrid teaching method which they reject; there is no correlation between poor acceptability of the ILE and different spheres of students' self-efficacy; and the variability of attention declared by students varies according to certain factors common to those detected by artificial intelligence (type of language, slide duration…).Discussion. The internal and external validities of this work highlight the close links between interest, motivation, engagement by identification, and attention. It is thus possible to put forward principles of pedagogical engineering in health within the framework of dematerialized courses focusing on the content, form and type of knowledge capsule. Finally, the health teacher must above all be “connected to” the students, so that technical developments can be adapted to their needs
Bassement, Jennifer. "Identification of fall-risk factors degradation using quality of balance measurements". Thesis, Troyes, 2014. http://www.theses.fr/2014TROY0035/document.
Texto completo da fonteFalls concern a third of the people aged over 65y and lead to the loss of functional ability. The detection of risks factors of falls is essential for early intervention. Six intrinsic risk factors of fall: vision, vestibular system, joint range of motion, leg muscle strength, joint proprioception and foot cutaneous proprioception were assessed with clinical tests before and after temporarily degradation. Standing balance was recorded on a force plate.From the force plate, 198 parameters of the centre of pressure displacement were computed. The parameters were used as variables to build neural network and logistic regression model for discriminating conditions. Feature selection analysis was performed to reduce the number of variables.Several models were built including 3 to 10 conditions. Models with 5 or less conditions appeared acceptable but better performance was found with models including 3 conditions. The best accuracy was 92% for a model including ankle range of motion, fatigue and vision contrast conditions. Qualities of balance parameters were able to diagnose impairments. However, the efficient models included only a few conditions. Models with more conditions could be built but would require a larger number of cases to reach high accuracy. The study showed that a neural network or a logistic model could be used for the diagnosis of balance impairments. Such a tool could seriously improve the prevention and rehabilitation practice
Guo, Jing. "Serious Games pour la e-Santé : application à la formation des médecins généralistes". Phd thesis, Toulouse 3, 2016. http://oatao.univ-toulouse.fr/17813/1/the%CC%80se_GUO.pdf.
Texto completo da fonteLivros sobre o assunto "Intelligence artificielle – Santé"
DESPATIN, Jane, Floriane PAX, Joseph TEDESCO e Valentin CHASSAGNE. Intelligence artificielle et innovations digitales en santé: Transformation des prises en charge et des métiers. BERGER LEVRAULT, 2021.
Encontre o texto completo da fonteDESPATIN, Jane, Floriane PAX, Joseph TEDESCO e Valentin CHASSAGNE. Intelligence artificielle et innovations digitales en santé: Transformation des prises en charge et des métiers. BERGER LEVRAULT, 2021.
Encontre o texto completo da fonteArtificial Intelligence and Machine Learning in Public Healthcare: Opportunities and Societal Impact. Springer Singapore Pte. Limited, 2021.
Encontre o texto completo da fonteTélémédecine Automatisée Par l'IA - une Nouvelle Méthode de Suivi à Distance des Patients: Intelligence Artificielle/augmentée Distribuée - IA - l'avenir des Soins de Santé. Independently Published, 2021.
Encontre o texto completo da fonteGoyal, Lalit Mohan. Artificial Intelligence and Internet of Things. Taylor & Francis Group, 2021.
Encontre o texto completo da fonteGoyal, Lalit Mohan, Tanzila Saba, Amjad Rehman e Souad Larabi-Marie-Sainte. Artificial Intelligence and Internet of Things: Applications in Smart Healthcare. Taylor & Francis Group, 2021.
Encontre o texto completo da fonteGoyal, Lalit Mohan, Tanzila Saba, Amjad Rehman e Souad Larabi-Marie-Sainte. Artificial Intelligence and Internet of Things: Applications in Smart Healthcare. Taylor & Francis Group, 2021.
Encontre o texto completo da fonteArtificial Intelligence and Internet of Things: Applications in Smart Healthcare. Taylor & Francis Group, 2021.
Encontre o texto completo da fonteLawry, Tom. AI in Health: A Leader's Guide to Winning in the New Age of Intelligent Health Systems. Healthcare Information & Management Systems Society, 2020.
Encontre o texto completo da fonteLawry, Tom. AI in Health: A Leader's Guide to Winning in the New Age of Intelligent Health Systems. Healthcare Information & Management Systems Society, 2020.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Intelligence artificielle – Santé"
Bonnet, Grégory. "Éthique et explicabilité en intelligence artificielle". In Santé connectée, 65–84. CNRS Éditions, 2020. http://dx.doi.org/10.4000/books.editionscnrs.45562.
Texto completo da fonteSabah, Gérard. "Intelligence artificielle et santé mentale". In Robots, de nouveaux partenaires de soins psychiques, 29. ERES, 2018. http://dx.doi.org/10.3917/eres.tisse.2018.02.0029.
Texto completo da fonteGanascia, Jean-Gabriel. "Éthique, intelligence artificielle et santé". In Traité de bioéthique, 527–40. Érès, 2018. http://dx.doi.org/10.3917/eres.hirsc.2018.01.0527.
Texto completo da fonteCharlet, Jean. "Intelligence artificielle et algorithmes en santé". In Traité de bioéthique, 541–54. Érès, 2018. http://dx.doi.org/10.3917/eres.hirsc.2018.01.0541.
Texto completo da fonteAYMEN CHALOUF, Mohamed, Hana MEJRI e Omessaad HAMDI. "Intelligence artificielle pour la sécurité en e-santé". In Gestion de la sécurité en e-santé, 213–35. ISTE Group, 2024. http://dx.doi.org/10.51926/iste.9179.ch9.
Texto completo da fonteChazard, Emmanuel. "Intelligence artificielle et aide à la décision en santé". In Algorithmes et décisions publiques, 57–78. CNRS Éditions, 2019. http://dx.doi.org/10.4000/books.editionscnrs.46172.
Texto completo da fontePOULLET, Yves. "Le numérique et le droit à la rencontre des personnes âgées". In Intelligence(s) artificielle(s) et Vulnérabilité(s) : kaléidoscope, 85–110. Editions des archives contemporaines, 2020. http://dx.doi.org/10.17184/eac.3639.
Texto completo da fonteTHOMAS-POHL, M., D. ROGEZ, L. BORRINI, D. AZOULAY, L. DARMON e É. LAPEYRE. "Les genoux prothétiques". In Médecine et Armées Vol. 44 No.4, 383–88. Editions des archives contemporaines, 2016. http://dx.doi.org/10.17184/eac.6830.
Texto completo da fonteGruson, David. "1 – Intelligence artificielle en santé : professionnels et patients au cœur de la régulation par la Garantie Humaine". In (in)hospitalités hospitalières, 173–81. Médecine & Hygiène, 2023. http://dx.doi.org/10.3917/mh.schaa.2023.01.0173.
Texto completo da fonteRelatórios de organizações sobre o assunto "Intelligence artificielle – Santé"
Mörch, Carl-Maria, Pascale Lehoux, Marc-Antoine Dilhac, Catherine Régis e Xavier Dionne. Recommandations pratiques pour une utilisation responsable de l’intelligence artificielle en santé mentale en contexte de pandémie. Observatoire international sur les impacts sociétaux de l’intelligence artificielle et du numérique, dezembro de 2020. http://dx.doi.org/10.61737/mqaf7428.
Texto completo da fonteGentelet, Karine, e Alexandra Bahary-Dionne. Les angles morts des réponses technologiques à la pandémie de COVID-19 : Disjonction entre les inégalités en santé et numériques structurantes de la marginalisation de certaines populations. Observatoire international sur les impacts sociétaux de l’intelligence artificielle et du numérique, setembro de 2020. http://dx.doi.org/10.61737/gsjs3130.
Texto completo da fonte