Artificial intelligence in healthcare institutions: A systematic literature review on influencing factors

Roppelt, Julia Stefanie; Kanbach, Dominik K.; Kraus, Sascha

doi:10.1016/j.techsoc.2023.102443

Cited by 18 publications

(4 citation statements)

References 89 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Organizational determinants also play a crucial role. The need to deal with uncertainties related to the use and integration of AI, and the ambiguity of AI contributes to distrust among professionals [47] . Thus, it is imperative that AI implementation processes be accompanied by clear and effective change management strategies, aiming to minimize organizational resistance and maximize the acceptance of technology and innovation [32,37,47] .…”

Section: Discussionmentioning

confidence: 99%

“…It is also noteworthy that perceptions of AI vary and are influenced by multiple factors, including personal experiences and organizational expectations [41,50] . In this sense, future research should explore the influence of the organizational context on the adoption and use of AI, as well as defining and identifying the necessary strategies to align new technologies with the needs and expectations of various stakeholders [33,35,47] .…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Use of Artificial Intelligence tools in supporting decision-making in hospital management

Alves,

Seringa,

Silvestre

et al. 2024

Preprint

View full text Add to dashboard Cite

Background The integration of Artificial Intelligence (AI) tools in hospital management holds potential for enhancing decision-making processes. This study investigates the current state of decision-making in hospital management, explores the potential benefits of AI integration, and examines hospital managers' perceptions of AI as a decision-support tool. Methods A descriptive and exploratory study was conducted using a qualitative approach. Data were collected through semi-structured interviews with 15 hospital managers from various departments and institutions. The interviews were transcribed, anonymized, and analyzed using thematic coding to identify key themes and patterns in the responses. Results Hospital managers highlighted the current inefficiencies in decision-making processes, often characterized by poor communication, isolated decision-making, and limited data access. The use of traditional tools like spreadsheet applications and business intelligence systems remains prevalent, but there is a clear need for more advanced, integrated solutions. Managers expressed both optimism and skepticism about AI, acknowledging its potential to improve efficiency and decision-making while raising concerns about data privacy, ethical issues, and the loss of human empathy. The study identified key challenges, including the variability in technical skills, data fragmentation, and resistance to change. Managers emphasized the importance of robust data infrastructure and adequate training to ensure successful AI integration. Conclusions The study reveals a complex landscape where the potential benefits of AI in hospital management are balanced with significant challenges and concerns. Effective integration of AI requires addressing technical, ethical, and cultural issues, with a focus on maintaining human elements in decision-making. AI is seen as a powerful tool to support, not replace, human judgment in hospital management, promising improvements in efficiency, data accessibility, and analytical capacity. Preparing healthcare institutions with the necessary infrastructure and providing specialized training for managers are crucial for maximizing the benefits of AI while mitigating associated risks.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Use of Artificial Intelligence tools in supporting decision-making in hospital management

Alves,

Seringa,

Silvestre

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…AI enables healthcare providers to optimize resource allocation, eliminate inefficiencies, make proactive health decisions, anticipate maintenance needs in medical equipment, and deliver more personalized patient care. The adoption of AI techniques in healthcare allows institutions to evolve, adapt, and differentiate themselves, thereby securing a competitive advantage in the healthcare sector [41]. This ILR highlights the necessity to bridge the existing knowledge gap by offering insights into the practical applications and benefits of AI in the French healthcare context, ensuring that stakeholders understand its transformative potential and the opportunities it presents for advancing healthcare delivery and patient care.…”

Section: Theoretical/conceptual Frameworkmentioning

confidence: 98%

“…One striking conclusion is the gap between AI's theoretical promise and its actual deployment in French healthcare settings, mainly owing to the difficulty of integrating advanced AI systems with existing hospital IT infrastructures. Bridging such a gap is costly and logistically complex, necessitating significant adjustments to data management systems to ensure compatibility and efficiency [41]. The exorbitant cost of such upgrades and the necessity for substantial training programs for healthcare professionals impede AI integration.…”

Section: Critique Of the Extant Literature To Identify The Future Of ...mentioning

confidence: 99%

AI-Driven Healthcare in France

2024

IJFMR

View full text Add to dashboard Cite

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.

show abstract

Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis

Das,

Mazumdar,

Khondakar

et al. 2024

BMEMat

View full text Add to dashboard Cite

Machine learning (ML) and nanotechnology interfacing are exploring opportunities for cancer treatment strategies. To improve cancer therapy, this article investigates the synergistic combination of Graphene Oxide (GO)‐based devices with ML techniques. The production techniques and functionalization tactics used to modify the physicochemical characteristics of GO for specific drug delivery are explained at the outset of the investigation. GO is a great option for treating cancer because of its natural biocompatibility and capacity to absorb medicinal chemicals. Then, complicated biological data are analyzed using ML algorithms, which make it possible to identify the best medicine formulations and individualized treatment plans depending on each patient's particular characteristics. The study also looks at optimizing and predicting the interactions between GO carriers and cancer cells using ML. Predictive modeling helps ensure effective payload release and therapeutic efficacy in the design of customized drug delivery systems. Furthermore, tracking treatment outcomes in real time is made possible by ML algorithms, which permit adaptive modifications to therapy regimens. By optimizing medication doses and delivery settings, the combination of ML and GO in cancer therapy not only decreases adverse effects but also enhances treatment accuracy.

show abstract

Artificial intelligence in healthcare institutions: A systematic literature review on influencing factors

Cited by 18 publications

References 89 publications

Use of Artificial Intelligence tools in supporting decision-making in hospital management

Use of Artificial Intelligence tools in supporting decision-making in hospital management

AI-Driven Healthcare in France

Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis

Contact Info

Product

Resources

About