Artificial Intelligence in Healthcare: Doctor as a Stakeholder

Bhavaraju, Subba Rao

doi:10.5772/intechopen.111490

Cited by 2 publications

(2 citation statements)

References 62 publications

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“…According to the participants, AI holds the potential to impact healthcare delivery in various aspects. However, the scope of applications of AI is limited to cases that are not complex, fit the predictive model, and do not require emotional or human aspects, like end-of-life and breaking bad news [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

Clinicians’ Views on Using Artificial Intelligence in Healthcare: Opportunities, Challenges, and Beyond

Alanazi

2023

Cureus

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Introduction: The healthcare industry has made significant progress in information technology, which has improved healthcare procedures and brought about advancements in clinical care services. This includes gathering crucial clinical data and implementing intelligent health information management. Artificial Intelligence (AI) has the potential to bolster further existing health information systems, notably electronic health records (EHRs). With AI, EHRs can offer more customized and adaptable roles for patients. This study aims to delve into the current and potential uses of AI and examine the obstacles that come with it. Method: In this study, we employed a qualitative methodology and purposive sampling to select participants. We sought out clinicians who were eager to share their professional insights. Our research involved conducting three focus group interviews, each lasting an hour. The moderator began each session by introducing the study's goals and assuring participants of confidentiality to foster a collaborative environment. The facilitator asked open-ended questions about EHR, including its applications, challenges, and AI-assisted features. Results: The research conducted by 26 participants has identified five crucial areas of using AI in healthcare delivery. These areas include predictive analysis, clinical decision support systems, data visualization, natural language processing (NLP), patient monitoring, mobile technology, and future and emerging trends. However, the hype surrounding AI and the fact that the technology is still in its early stages pose significant challenges. Technical limitations related to language processing and context-specific reasoning must be addressed. Furthermore, medico-legal challenges arise when AI supports or autonomously delivers healthcare services. Governments must develop strategies to ensure AI's responsible and transparent application in healthcare delivery. Conclusion: AI technology has the potential to revolutionize healthcare through its integration with EHRs and other existing technologies. However, several challenges must be addressed before this potential can be fully realized. The development and testing of complex EHR systems that utilize AI must be approached with care to ensure their accuracy and trustworthiness in decision-making about patient treatment. Additionally, there is a need to navigate medico-legal obligations and ensure that benefits are equitably distributed.

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Section: Discussionmentioning

confidence: 99%

Clinicians’ Views on Using Artificial Intelligence in Healthcare: Opportunities, Challenges, and Beyond

Alanazi

2023

Cureus

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“…Techniques within ML include supervised learning, unsupervised learning, and reinforcement learning. Natural language processing enables AI systems to understand, interpret, and respond to human language, covering tasks like speech recognition, language translation, sentiment analysis, and text generation [14]. Computer vision allows AI to interpret and process visual information, such as recognizing objects, faces, and scenes in images and videos.…”

Section: Introductionmentioning

confidence: 99%

Greening Automation: Policy Recommendations for Sustainable Development in AI-Driven Industries

Doran,

Badareu,

Doran

et al. 2024

Sustainability

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This study delves into the dynamic relationship between artificial intelligence (AI) and environmental performance, with a specific focus on greenhouse gas (GHG) emissions across European countries from 2012 to 2022. Utilizing data on industrial robots, AI companies, and AI investments, we examine how AI adoption influences GHG emissions. Preliminary analyses, including ordinary least squares (OLS) regression and diagnostic assessments, were conducted to ensure data adequacy and model readiness. Subsequently, the Elastic Net (ENET) regression model was employed to mitigate overfitting issues and enhance model robustness. Our findings reveal intriguing trends, such as a downward trajectory in GHG emissions correlating with increased AI investment levels and industrial robot deployment. Graphical representations further elucidate the evolution of coefficients and cross-validation errors, providing valuable insights into the relationship between AI and environmental sustainability. These findings offer policymakers actionable insights for leveraging AI technologies to foster sustainable development strategies.

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Artificial Intelligence in Healthcare: Doctor as a Stakeholder

Cited by 2 publications

References 62 publications

Clinicians’ Views on Using Artificial Intelligence in Healthcare: Opportunities, Challenges, and Beyond

Clinicians’ Views on Using Artificial Intelligence in Healthcare: Opportunities, Challenges, and Beyond

Greening Automation: Policy Recommendations for Sustainable Development in AI-Driven Industries

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