HALP score-adjusted FRAXplus tool effectively predicts hip fracture risk in elderly women with type 2 diabetes mellitus.

Artificial intelligence (AI) is revolutionizing medicine, improving the performance of physicians, and delivering quality care to patients []. The effectiveness of AI in healthcare delivery is expanding and could soon be used in important aspects of clinical decision-making []. The emergence of computer hardware and software in medicine, the increasing use of technology, and the digitization of patient records are important factors in the emergence of AI in healthcare and the clinic. These developments provide new opportunities and challenges in healthcare [], ranging from identifying new relationships between genetic codes or controlling surgical robots to easily accessing patient medical information [, ]. AI ethics is a new branch of digital ethics that has emerged in response to growing concerns about the impact of AI []. Over the past five years, guidelines for AI ethics have been published, but further research is needed to develop a global consensus on a coherent standard for the ethics of AI []. Several prominent ethical frameworks have been developed to guide responsible AI deployment. The European Commission’s High-Level Expert Group on AI published “Ethics Guidelines for Trustworthy AI” (2019), which identifies seven key requirements including human agency and oversight, transparency, and accountability []. The World Health Organization (WHO) released “Ethics and Governance of Artificial Intelligence for Health” (2021), emphasizing six guiding principles: protecting human autonomy, promoting well-being, ensuring transparency, fostering accountability, ensuring equity, and promoting sustainable AI []. The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems proposed “Ethically Aligned Design” (2019), which prioritizes human well-being as a metric for system development []. Additionally, the Asilomar AI Principles (2017) and the OECD Principles on AI (2019) provide foundational guidance on safety, fairness, and accountability [, ]. Despite these efforts, Hagendorff (2020) found that many of these guidelines lack actionable enforcement mechanisms, particularly regarding the ethical principles of beneficence, nonmaleficence, and justice []. Autonomy is a core moral value deeply rooted in the ethical, legal, and political practices of many societies, and the development and deployment of AI has raised new questions about the effects of AI on human autonomy []. Autonomy has been identified as a critical aspect of justice and well-being, playing a role in individuals’ mental health, and the effects of digital experiences on human autonomy are neither simple nor fixed []. The application of AI, from consent to data collection and processing to institutional, social, and other related considerations, must be examined to fully understand the impact of AI on human autonomy, which is yet to be completely understood []. Patients prefer the use of AI to help them make judgments and choices that are based on their own free will, but this also comes with significant challenges []. Among the most significant ethical challenges associated with the use of AI in healthcare is the lack of transparency of machine learning algorithms—commonly referred to as the “black box” problem. This opacity is thought to prevent patients from fully understanding the rationale behind AI-generated medical recommendations, thereby undermining their capacity to make truly autonomous and informed healthcare decisions [–]. The rapid rise of AI in healthcare promises a new era in medicine, but this new technology also raises substantial ethical questions, the most important of which is how to maintain patients’ autonomy [].