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  • Ethical and social considerations of applying artificial intelligence . . .
    This article presents a scoping review of the ethical and social issues pertaining to AI in healthcare, with a novel two-pronged design One strand of the review (SR1) consists of a broad review of the academic literature restricted to a recent timeframe (2021–23), to better capture up to date developments and debates
  • Fairness of artificial intelligence in healthcare: review and . . .
    In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health
  • Algorithmic individual fairness and healthcare: a scoping review
    Based on the 32 articles in the review, we identified several themes, including philosophical underpinnings of fairness, IF metrics, mitigation methods for achieving IF, implications of achieving IF on group fairness and vice versa, and applications of IF in healthcare
  • AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias
    This review paper examines the integration of AI in healthcare, highlighting critical challenges related to bias and exploring strategies for mitigation We emphasize the necessity of diverse datasets, fairness-aware algorithms, and regulatory frameworks to ensure equitable healthcare delivery
  • AI-driven healthcare: Fairness in AI healthcare: A survey
    We adopt this three-stage framework to capture the end-to-end process of data collection, model development, and decision-making in AI healthcare systems This structure, widely used in the fairness literature, supports systematic bias analysis and enables targeted detection and mitigation strategies at each stage [ 59 – 64 ]
  • Healthcare Bias in AI: A Systematic Literature Review
    By rigorously analyzing peer-reviewed studies based on inclusion and exclusion criteria, this review identies the populations most impacted by bias and explores the diversity of existing mit- igation strategies, fairness metrics, and ethical frameworks
  • A scoping review and evidence gap analysis of clinical AI fairness
    We highlight the scarcity of AI fairness research in medical domains, the narrow focus on bias-relevant attributes, the dominance of group fairness centering on model performance equality, and the limited integration of clinician-in-the-loop to improve AI fairness
  • Algorithmic Individual Fairness and Healthcare: A Scoping Review
    We conducted a scoping review on algorithmic individual fairness to understand the current state of research in the metrics and methods developed to achieve individual fairness and its applications in healthcare
  • Bias and fairness in AI-driven healthcare: Addressing disparities in . . .
    Healthcare outcomes experience diverse anomalies when AI algorithms carry bias, which produces specific harm to marginalized populations Microbiological diagnosis biases originate from three core elements: imbalanced data collection methods, ineffective model training practices and structural healthcare deficiencies
  • Practical, epistemic and normative implications of algorithmic bias in . . .
    Background There is a growing concern about artificial intelligence (AI) applications in healthcare that can disadvantage already under-represented and marginalised groups (eg, based on gender or race) Objectives Our objectives are to canvas the range of strategies stakeholders endorse in attempting to mitigate algorithmic bias, and to consider the ethical question of responsibility for algorithmic bias Methodology The study involves in-depth, semistructured interviews with healthcare





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