Enjeux du déploiement de l’intelligence artificielle en santé

Callegarin, Davide; Callier, Patrick

doi:10.1016/j.actpha.2021.10.006

Cited by 2 publications

(8 citation statements)

References 14 publications

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“…Despite the recognized global importance of AI in revolutionizing healthcare delivery and enhancing patient care, research on its application in France is not as comprehensive. This research gap hinders a complete understanding of the challenges and opportunities faced by healthcare providers in adopting AI technologies [1]. Addressing this knowledge gap is crucial as it informs healthcare policymakers, administrators, and academics about the specific hurdles and benefits of AI integration in the French healthcare context.…”

Section: Theoretical/conceptual Frameworkmentioning

confidence: 99%

“…Under the stringent guidelines of the General Data Protection Regulation (GDPR), healthcare providers face complex challenges in managing sensitive patient information within AI systems while safeguarding privacy rights. The need for interoperability between various health information systems also becomes crucial for seamless data integration, which is essential for AI's optimal functionality [1]. The financial and logistical demands of modernizing existing infrastructures to support AI functionalities also present substantial hurdles.…”

Section: Transition To Personalized Medicine Facilitated By Aimentioning

confidence: 99%

“…The French healthcare system is undergoing a significant transformation by integrating artificial intelligence (AI) technologies to position itself as a world leader in medical innovation [1]. French Healthcare professionals are leveraging AI and data-powered technologies to enhance healthcare delivery, improve diagnosis accuracy, accelerate the detection of diseases, and provide more personalized care.…”

Section: Introductionmentioning

confidence: 99%

“…These collaborative efforts are essential in overcoming current obstacles faced by the healthcare sector and laying the groundwork for a proactive, patient-centered healthcare system. Through these initiatives, France has been making significant efforts to position itself as a leader in AI-powered healthcare innovation [1]. Overall, the proactive use of AI technologies in the French healthcare system exemplifies a solid commitment to enhancing medical practices and improving patient care.…”

Section: Introductionmentioning

confidence: 99%

“…These AI technologies are not merely supplementing established healthcare practices but also setting new standards for how healthcare is delivered, monitored, and evolved. In healthcare, the incorporation of AI technologies has significantly accelerated the French healthcare system's shift towards personalized medicine, marking a new era in medical treatment where care is precisely tailored to each individual's genetic and physiological characteristics [1]. AI's capability to synthesize and analyze vast amounts of genetic data and diverse patient-specific information has profoundly transformed personalized medicine.…”

Section: Introductionmentioning

confidence: 99%

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AI-Driven Healthcare in France

2024

IJFMR

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This integrative literature review (ILR) looks into the use of artificial intelligence (AI) technology in the French healthcare system, emphasizing personalized medicine and predictive health analytics. The study subject is the problematic integration of AI technologies, which is hampered by significant challenges, including data privacy concerns, system interoperability, ethical and legal issues, resistance to technological change, and the need for extensive training of healthcare professionals. These issues affect French healthcare professionals and politicians, who must overcome these challenges to utilize AI's potential fully. This research aims to investigate and assess the integration of AI into the French healthcare system, particularly in personalized medicine and predictive health analytics, to identify and address the obstacles and leverage the opportunities that improve patient care and operational efficiency. The ILR's guiding conceptual framework is based on three essential concepts: artificial intelligence, personalized medicine, and predictive healthcare analytics. The research methodology consists in thoroughly examining current literature, qualitative study of case studies, and interviews with industry professionals. The study's findings show that AI has tremendous potential to increase diagnostic precision and treatment accuracy in the French healthcare system. They highlight initiatives such as establishing specialized departments like "Intelligent Healthcare Services" and creating positions like "Intelligent Doctor". The conclusions address the potential implications of the findings for enhancing patient care and operational efficiency, as well as recommendations for further study and practice. These include performing longitudinal studies, looking into emerging data integrity solutions such as blockchain, and improving healthcare professional training programs. Ultimately, breaking down current obstacles and ensuring that AI technologies improve healthcare delivery and patient outcomes would make France a leader in AI-driven medical innovation.

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Section: Theoretical/conceptual Frameworkmentioning

confidence: 99%

Section: Transition To Personalized Medicine Facilitated By Aimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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AI-Driven Healthcare in France

2024

IJFMR

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Building a Recommender System to Predict the Shape of Bacteria in Urine Cytobacteriological Examination Using Machine Learning

Lafraxo,

Hami,

Merrakchi

et al. 2023

Int. J. Onl. Eng.

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This study aimed to build a recommender system that predicts the shape of bacteria for biological requests of urine cytobacteriological examination (UCBE) using machine learning techniques, to reduce the time taken to identify the shape of bacteria (Cocci or Bacilli). We used different methods and techniques in the process: Unified Modelling Language (UML) was used for digital design architecture, Rstudio tool with R programming language for system development, and Random Forest (RF) algorithm for the prediction. Experimental results showed that the time needed to identify the shape of bacteria is decreased, and bacilli bacteria are better recognized by the algorithm with an error rate of 3%. In addition to that, the proposed recommender system allows biologists to validate and correct the prediction and improve the accuracy of the classification algorithm used in the future.

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Enjeux du déploiement de l’intelligence artificielle en santé

Cited by 2 publications

References 14 publications

AI-Driven Healthcare in France

AI-Driven Healthcare in France

Building a Recommender System to Predict the Shape of Bacteria in Urine Cytobacteriological Examination Using Machine Learning

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