Graph Interpretation, Summarization and Visualization Techniques: A Review and Open Research Issues

Mishra, Prashant; Kumar, Santosh; Chaube, Mithilesh Kumar

doi:10.1007/s11042-021-11582-9

Cited by 10 publications

(1 citation statement)

References 159 publications

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“…Therefore, interpretation-based methods using AI emerged as a promising solution. 48 One possible application of AI in the computerized interpretation of graphs in the clinical field is to use machine learning algorithms to analyze electrocardiogram signals. 49 A study evaluated the performance of AI facilitating the interpretation of cardiotocographs through image pattern recognition by applying computerized interpretation.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews

Khosravi,

Zare,

Mojtabaeian

et al. 2024

Health Services Research and Managerial Epidemiology

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Introduction The use of artificial intelligence (AI), which can emulate human intelligence and enhance clinical results, has grown in healthcare decision-making due to the digitalization effects and the COVID-19 pandemic. The purpose of this study was to determine the scope of applications of AI tools in the decision-making process in healthcare service delivery networks. Materials and methods This study used a qualitative method to conduct a systematic review of the existing reviews. Review articles published between 2000 and 2024 in English-language were searched in PubMed, Scopus, ProQuest, and Cochrane databases. The CASP (Critical Appraisal Skills Programme) Checklist for Systematic Reviews was used to evaluate the quality of the articles. Based on the eligibility criteria, the final articles were selected and the data extraction was done independently by 2 authors. Finally, the thematic analysis approach was used to analyze the data extracted from the selected articles. Results Of the 14 219 identified records, 18 review articles were eligible and included in the analysis, which covered the findings of 669 other articles. The quality assessment score of all reviewed articles was high. And, the thematic analysis of the data identified 3 main themes including clinical decision-making, organizational decision-making, and shared decision-making; which originated from 8 subthemes. Conclusions This study revealed that AI tools have been applied in various aspects of healthcare decision-making. The use of AI can improve the quality, efficiency, and effectiveness of healthcare services by providing accurate, timely, and personalized information to support decision-making. Further research is needed to explore the best practices and standards for implementing AI in healthcare decision-making.

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Section: Discussionmentioning

confidence: 99%