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In the past, planning projects such as schools or public facilities required extensive manual effort to gather and compile data. Information such as property boundaries, soil characteristics, road networks, zoning regulations, and flood zones had to be sourced individually from courthouses, utility providers, and registry offices. Assembling these datasets into a coherent format often took several months, delaying project timelines.The introduction of Geographic Information Systems (GIS)...
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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Updated: Feb 14, 2026

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Ethical Responsibility in Medical AI: A Semi-Systematic Thematic Review and Multilevel Governance Model.

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  • 1ISLA Santarém, Polytechnic University, Rua Dr. Teixeira Guedes, 31, 2000-029 Santarém, Portugal.

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Summary

Artificial intelligence (AI) in healthcare presents ethical challenges. This study found a fragmented landscape, highlighting the need for integrated governance frameworks to address transparency, accountability, and equity in AI-assisted medicine.

Keywords:
algorithmic accountabilitydata protection and privacyethical responsibilityjustice and equitymedical AIpatient autonomyregulatory governancetransparency and explainability

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Area of Science:

  • Medical Ethics
  • Health Informatics
  • Artificial Intelligence in Medicine

Background:

  • Artificial intelligence (AI) is revolutionizing healthcare, improving diagnostics and efficiency.
  • This transformation introduces significant ethical considerations, including transparency, accountability, and fairness.
  • The study investigates how ethical responsibility is conceptualized and distributed within AI-assisted healthcare literature.

Purpose of the Study:

  • To examine the literature's conceptualization and distribution of ethical responsibility in AI-assisted healthcare.
  • To identify dominant ethical concerns and gaps in current AI healthcare practices.
  • To develop a multilevel ethical responsibility model for AI in medicine.

Main Methods:

  • A semi-systematic, theory-informed thematic review adhering to PRISMA 2020 guidelines.
  • Literature search from 2020-2025 across major scientific databases (PubMed, ScienceDirect, IEEE Xplore) and MDPI journals.
  • Analysis of 187 high-relevance studies using an eight-category ethical framework, including transparency, accountability, and patient autonomy.

Main Results:

  • A fragmented ethical landscape where technological advancement outpaces regulatory harmonization and shared accountability.
  • Transparency and explainability emerged as primary concerns (34.8%), with significant gaps identified in organizational responsibility, data equity, and patient autonomy.
  • Development of a multilevel ethical responsibility model integrating clinical, institutional, and regulatory dimensions.

Conclusions:

  • AI in medicine necessitates robust governance frameworks that align ethical principles with regulatory practices and ensure epistemic justice.
  • A proposed multidimensional model bridges normative ethics and operational governance for AI implementation.
  • Future research should focus on empirical, longitudinal, and interdisciplinary studies to evaluate AI's impact on clinical practice, equity, and trust.