Connecting algorithmic fairness and fair outcomes in a sociotechnical simulation case study of AI-assisted healthcare.

The convergence of healthcare with the Internet of Things (IoT) and Artificial Intelligence (AI) is reshaping medical practice with promising enhanced data-driven insights, automated decision-making, and remote patient monitoring. It has the transformative potential of these technologies to revolutionize diagnosis, treatment, and patient care. This study aims to explore the integration of IoT and AI in healthcare, outlining their applications, benefits, challenges, and potential risks. By synthesizing existing literature, this study aims to provide insights into the current landscape of AI, IoT, and AIoT in healthcare, identify areas for future research and development, and establish a framework for the effective use of AI in health. A comprehensive literature review included indexed databases such as PubMed/Medline, Scopus, and Google Scholar. Key search terms related to IoT, AI, healthcare, and medicine were employed to identify relevant studies. Papers were screened based on their relevance to the specified themes, and eventually, a selected number of papers were methodically chosen for this review. The integration of IoT and AI in healthcare offers significant advancements, including remote patient monitoring, personalized medicine, and operational efficiency. Wearable sensors, cloud-based data storage, and AI-driven algorithms enable real-time data collection, disease diagnosis, and treatment planning. However, challenges such as data privacy, algorithmic bias, and regulatory compliance must be addressed to ensure responsible deployment of these technologies. Integrating IoT and AI in healthcare holds immense promise for improving patient outcomes and optimizing healthcare delivery. Despite challenges such as data privacy concerns and algorithmic biases, the transformative potential of these technologies cannot be overstated. Clear governance frameworks, transparent AI decision-making processes, and ethical considerations are essential to mitigate risks and harness the full benefits of IoT and AI in healthcare.

Increasing concerns have been raised regarding artificial intelligence (AI) bias, and in response, efforts have been made to pursue AI fairness. In this paper, we argue that the idea of structural injustice serves as a helpful framework for clarifying the ethical concerns surrounding AI bias—including the nature of its moral problem and the responsibility for addressing it—and reconceptualizing the approach to pursuing AI fairness. Using AI in health care as a case study, we argue that AI bias is a form of structural injustice that exists when AI systems interact with other social factors to exacerbate existing social inequalities, making some groups of people more vulnerable to undeserved burdens while conferring unearned benefits to others. The goal of AI fairness, understood this way, is to pursue a more just social structure with the development and use of AI systems when appropriate. We further argue that all participating agents in the unjust social structure associated with AI bias bear a shared responsibility to join collective action with the goal of reforming the social structure, and we provide a list of practical recommendations for agents in various social positions to contribute to this collective action.

Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model development, with potential for exacerbating health disparities. However, bias in imaging AI is a complex topic that encompasses multiple coexisting definitions. Bias may refer to unequal preference to a person or group owing to preexisting attitudes or beliefs, either intentional or unintentional. However, cognitive bias refers to systematic deviation from objective judgment due to reliance on heuristics, and statistical bias refers to differences between true and expected values, commonly manifesting as systematic error in model prediction (ie, a model with output unrepresentative of real-world conditions). Clinical decisions informed by biased models may lead to patient harm due to action on inaccurate AI results or exacerbate health inequities due to differing performance among patient populations. However, while inequitable bias can harm patients in this context, a mindful approach leveraging equitable bias can address underrepresentation of minority groups or rare diseases. Radiologists should also be aware of bias after AI deployment such as automation bias, or a tendency to agree with automated decisions despite contrary evidence. Understanding common sources of imaging AI bias and the consequences of using biased models can guide preventive measures to mitigate its impact. Accordingly, the authors focus on sources of bias at stages along the imaging machine learning life cycle, attempting to simplify potentially intimidating technical terminology for general radiologists using AI tools in practice or collaborating with data scientists and engineers for AI tool development. The authors review definitions of bias in AI, describe common sources of bias, and present recommendations to guide quality control measures to mitigate the impact of bias in imaging AI. Understanding the terms featured in this article will enable a proactive approach to identifying and mitigating bias in imaging AI. Published under a CC BY 4.0 license. Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Rouzrokh and Erickson in this issue.

Objective: The objective of this study is to examine the civil liability implications of errors associated with artificial intelligence (AI) technologies in healthcare, with the aim of proposing comprehensive legal frameworks to regulate AI’s integration into the sector. This research seeks to align AI advancements with international standards and sustainable development goals, particularly SDG 3 (Good Health and Well-Being) and SDG 16 (Peace, Justice, and Strong Institutions). Theoretical Framework: The research is grounded in international human rights conventions, national legislation on data protection, and frameworks such as the World Health Organization’s guidelines and the Council of Europe Framework Convention on Artificial Intelligence, Human Rights, Democracy, and the Rule of Law. These provide a robust foundation for exploring the legal and ethical dimensions of AI in healthcare. Method: A qualitative research approach was adopted, analysing legal documents, international standards, and case studies to assess the regulatory challenges of AI in healthcare. The study also reviews best practices from jurisdictions effectively addressing AI’s legal and ethical implications. Results and Discussion: The findings reveal significant gaps in existing legal frameworks governing AI in healthcare, particularly in areas of accountability and data protection. The discussion contextualizes these gaps within the theoretical framework, emphasizing the need for proactive legislative measures. Challenges such as algorithmic bias, transparency, and equitable access to AI technologies are critically examined. Research Implications: The study underscores the necessity for Omani legislators to adopt robust regulatory measures, ensuring accountability and alignment with international best practices. These measures can enhance healthcare quality and contribute to sustainable development. Originality/Value: This study contributes to the literature by addressing the underexplored legal and ethical challenges of AI in healthcare within the context of sustainable development. It offers actionable recommendations for policymakers, emphasizing the responsible and ethical use of AI to advance medical innovation and protect human rights.

Artificial Intelligence (AI) is revolutionizing healthcare by enhancing patient outcomes, reducing costs, and increasing the efficiency of medical professionals. This mini-review explores the diverse applications of AI in healthcare, including disease diagnosis, personalized treatment plans, and patient survival rate predictions. AI technologies such as Machine Learning (ML), deep learning, Natural Language Processing (NLP), and Robotic Process Automation (RPA) are becoming integral to modern healthcare practices. These technologies enable early disease detection, particularly in cases like cancer, by analyzing medical images and patient data, leading to more effective and personalized treatment strategies. Additionally, AI can predict patient outcomes by analyzing large datasets from electronic health records, providing valuable insights that can inform clinical decisions. However, the integration of AI in healthcare also presents significant ethical challenges. Issues such as data privacy, algorithmic bias, lack of transparency, and the potential for increased health inequalities need to be addressed. The World Health Organization (WHO) has provided guidelines emphasizing the ethical use of AI, highlighting the importance of designing AI systems that respect human rights and promote equity. As AI continues to advance, it is crucial to ensure its responsible and transparent use to maximize benefits and minimize risks. This review underscores the transformative potential of AI in healthcare while calling for vigilant ethical considerations to ensure that AI technologies are implemented in a manner that enhances patient care and upholds ethical standards.

The integration of artificial intelligence (AI) technology is causing a shift in the healthcare sector. Although artificial intelligence (AI) has enormous promise to improve patient care, diagnostics, and operational efficiency, its application must be handled carefully to avoid moral problems. This paper explores the important factors to take into account when implementing Responsible AI in healthcare environments, with a focus on accountability, justice, openness, and privacy. It looks at how AI may enhance clinical judgment, expedite medical procedures, and provide individualized care. Nonetheless, there are still a lot of obstacles to overcome, including data security, bias mitigation, and regulatory compliance. Healthcare businesses are able to take use of AI while maintaining safe and fair patient outcomes by implementing strong governance and ethical regulations. The research also looks at best practices, legal frameworks, and case studies from the real world that are influencing the direction of responsible AI in healthcare.

The integration of artificial intelligence (AI) in healthcare has the potential to revolutionize the industry by improving patient outcomes and increasing efficiency. However, the rapid development and implementation of AI technologies raise complex legal issues and challenges. This article explores the key legal aspects of AI integration in healthcare, including data privacy and security, liability and accountability, intellectual property, and regulatory compliance. It examines relevant international and national legal instruments, regulations, and guidelines, as well as industry-specific standards that apply to AI in healthcare. The study also analyzes case studies and practical applications to highlight legal challenges and resolutions, lessons learned, and best practices. The discussion addresses the implications of the results, comparing the legal landscape for AI in healthcare to other industries and countries and highlighting potential future legal developments and challenges. The conclusion summarizes key findings, offers recommendations for integrating AI in healthcare systems while addressing legal concerns, and proposes future directions for legal research and policy development in the context of AI and healthcare. This comprehensive analysis aims to inform healthcare providers, AI developers, and policymakers on the legal landscape surrounding AI in healthcare, providing valuable insights to navigate this complex domain and harness the potential of AI to transform healthcare delivery.

BackgroundThe integration of artificial intelligence (AI) holds substantial potential to alter diagnostics and treatment in health care settings. However, public attitudes toward AI, including trust and risk perception, are key to its ethical and effective adoption. Despite growing interest, empirical research on the factors shaping public support for AI in health care (particularly in large-scale, representative contexts) remains limited.ObjectiveThis study aimed to investigate public attitudes toward AI in patient health care, focusing on how AI attributes (autonomy, costs, reliability, and transparency) shape perceptions of support, risk, and personalized care. In addition, it examines the moderating role of sociodemographic characteristics (gender, age, educational level, migration background, and subjective health status) in these evaluations. Our study offers novel insights into the relative importance of AI system characteristics for public attitudes and acceptance.MethodsWe conducted a factorial vignette experiment with a probability-based survey of 3030 participants from Germany’s general population. Respondents were presented with hypothetical scenarios involving AI applications in diagnosis and treatment in a hospital setting. Linear regression models assessed the relative influence of AI attributes on the dependent variables (support, risk perception, and personalized care), with additional subgroup analyses to explore heterogeneity by sociodemographic characteristics.ResultsMean values between 4.2 and 4.4 on a 1-7 scale indicate a generally neutral to slightly negative stance toward AI integration in terms of general support, risk perception, and personalized care expectations, with responses spanning the full scale from strong support to strong opposition. Among the 4 dimensions, reliability emerges as the most influential factor (percentage of explained variance [EV] of up to 10.5%). Respondents expect AI to not only prevent errors but also exceed current reliability standards while strongly disapproving of nontraceable systems (transparency is another important factor, percentage of EV of up to 4%). Costs and autonomy play a comparatively minor role (percentage of EVs of up to 1.5% and 1.3%), with preferences favoring collaborative AI systems over autonomous ones, and higher costs generally leading to rejection. Heterogeneity analysis reveals limited sociodemographic differences, with education and migration background influencing attitudes toward transparency and autonomy, and gender differences primarily affecting cost-related perceptions. Overall, attitudes do not substantially differ between AI applications in diagnosis versus treatment.ConclusionsOur study fills a critical research gap by identifying the key factors that shape public trust and acceptance of AI in health care, particularly reliability, transparency, and patient-centered approaches. Our findings provide evidence-based recommendations for policy makers, health care providers, and AI developers to enhance trust and accountability, key concerns often overlooked in system development and real-world applications. The study highlights the need for targeted policy and educational initiatives to support the responsible integration of AI in patient care.

The integration of artificial intelligence (AI) in healthcare presents significant promise to enhance clinical procedures and patient outcomes. This research examines the setting, methodology, conclusions, and issues associated with AI in healthcare. The swift proliferation of digital health data, encompassing medical imaging and clinical records, has generated substantial prospects for AI applications. Artificial intelligence methodologies, including machine learning, natural language processing, and computer vision, facilitate the derivation of significant insights from intricate datasets, hence improving clinical decision-making. A thorough literature review examines the practical applications of AI, encompassing its roles in medical diagnostics, treatment planning, and patient outcome prediction. The report also examines ethical issues, data protection, and legal frameworks, which are crucial for the responsible application of AI in healthcare. The results illustrate AI's capacity to enhance diagnostic precision, facilitate administrative efficiency, and optimise resource distribution, resulting in tailored therapies and improved healthcare administration. Nonetheless, obstacles persist, such as data integrity, algorithm transparency, and ethical considerations, which must be resolved to guarantee the secure and efficient deployment of AI. Continuous research, cooperation between healthcare and AI experts, and the establishment of comprehensive regulatory frameworks are essential for optimising the advantages of AI while minimising hazards. This research highlights AI's capacity to transform healthcare, stressing the necessity for a multidisciplinary strategy to effectively harness its benefits and tackle the associated ethical and regulatory dilemmas.

Artificial Intelligence (AI) is revolutionizing modern healthcare by significantly enhancing clinical decision-making, streamlining operational workflows, and delivering advanced, patient-centered care (Smith & Zhao, 2021). This research article explores AI's pivotal role in contemporary healthcare systems, focusing on its ability to improve diagnostic accuracy, support personalized treatment planning, and enable predictive outcome modeling (Johnson et al., 2020). In particular, it examines the practical applications of AI in analyzing medical imaging, screening electronic health records (EHRs), conducting predictive analytics, and providing virtual health support each addressing persistent challenges in medical practice (Lee & Chen, 2019; Thomas & Arora, 2022). The study also evaluates the obstacles hindering AI's widespread integration, including ethical dilemmas, technical limitations, and regulatory challenges related to data privacy, algorithmic bias, and system compatibility (Nguyen, 2020; Patel & Green, 2023). Furthermore, the article explores the future trajectory of AI in healthcare, emphasizing the necessity for interdisciplinary collaboration between healthcare professionals and technology developers. Such cooperation is essential to harness AI’s full potential while safeguarding patient rights and promoting equitable access to high-quality healthcare services (Martin & Davis, 2021).

Objectives The use of artificial intelligence (AI) in healthcare is a growing field of research and clinical application. The views of the general public, ie future healthcare users, need to be surveyed and interpreted so that researchers and the public have a shared understanding of the appropriate use of AI. Currently, there is only limited data on the public’s views. Methods An anonymous, quantitative questionnaire was administered as part of a public exhibition on AI. The questionnaire was based on previously validated questions designed to assess respondents’ views on the use of AI in healthcare. Brief demographic data were also collected. Results The population surveyed was more diverse and younger than the general UK population (65% white, 45% aged 18-29). Respondents were largely comfortable with the application of AI in healthcare: 80% felt positively about its use, 56% thought it would be safe. 70% did not feel that it would replace doctors, and most would not be happy for AI to make decisions without considering their feelings. Conclusions Our study shows that the population we surveyed, particularly young future healthcare users, are comfortable with the use of AI in healthcare, but do not see it as a replacement for doctors. Advances in knowledge This paper highlights views from the general public on the use of AI in healthcare, which is largely under researched.

Advancing AI in healthcare: A comprehensive review of best practices

The growing role of Artificial Intelligence (AI) in healthcare has unlocked powerful capabilities for predicting and managing chronic diseases. However, there are some issues the first one is the widespread reliance on complex, opaque models limit their interpretability and, consequently, their acceptance in clinical decision-making. In parallel the second one is, the considerable computational requirements of such models raise concerns about energy use and long-term sustainability. This study presents a vision-driven framework that unites Explainable AI (XAI) with sustainable computing to develop practical and responsible diabetes prediction systems. Using the PIMA Indian Diabetes dataset, we compare the performance of logistic regression, decision tree, random forest, and deep learning models, assessing three key dimensions: predictive accuracy, interpretability through SHAP and LIME, and computational resource efficiency. Our results show that transparent, resource-conscious models can match or surpass the accuracy of high-complexity architectures while reducing environmental impact. The proposed approach demonstrates that AI in healthcare can be both trustworthy for practitioners and sustainable for the planet, setting a precedent for future medical AI systems.

The Promise Fund and Hologic Inc. have partnered to expand access to AI-supported breast cancer screening exams in a medically underserved population. As part of this initiative, patient focus groups and patient navigator interviews were conducted to explore patient experiences and perceptions related to breast health, mammography, and artificial intelligence (AI) in healthcare. These interviews were held, virtually, between February and March 2024, and focus groups were held, in-person, on April 9-10, 2024, with participants at FoundCare and the Community Health Center. FoundCare, a federally qualified health center (FQHC), and Community Health Center, a free clinic, both collaborate with the Promise Fund in promoting access to women’s breast and cervical health screenings, diagnostic follow up and cancer care services. The in-person sessions, attended by twenty patients, and the virtual interviews, attended by six patient navigators, were designed to represent diverse linguistic, racial, and cultural backgrounds, including multiple Spanish-speaking communities from Mexico, Dominican Republic, Guatemala, Chile, and Venezuela. All patient navigators were adult women from various free clinics and FQHCs. Patient participants were adult females, all at or below 200% of the Federal Poverty Guidelines. Key themes from the discussions included: 1. Health Information Sources: Patients primarily relied on the internet, community health centers, medical visits, family, and cultural community communications for health information. Some patients expressed difficulty in keeping up with health information and emphasized the importance of direct medical consultations with providers and preventive care. Patient navigators were a critical source of information once the patient could access and be connected to that resource. Clinical health and more upstream social services, such as transportation to health appointments and family food security, were also critical needs that patient navigators were key in facilitating. 2. Mammogram Experiences: Experiences with mammograms varied, with some patients reporting pain and discomfort, particularly those with implants. Regular screenings, family history of breast cancer, and personal vigilance were common motivators for mammograms. Financial barriers, insurance issues, and the need for patient advocacy were frequently highlighted. 3. AI in Healthcare: Patients had mixed levels of awareness and understanding of AI. Many associated AI with advanced diagnostics and potential improvements in early disease detection and surgical precision. Concerns included the potential loss of personal interactions with healthcare providers, privacy issues, and the fear of job displacement. However, patients had key opinions on AI that were more favorable than anticipated. There was optimism about AI’s ability to enhance diagnostic accuracy, patient experience, provider trustworthiness, and treatment outcomes. Additionally, both patients and patient navigators preferred that physicians and AI work together. Patient navigators were even more familiar with AI. Given the critical role patient navigators hold, their knowledge may play an integral role in improving patient education and access to new technologies in the future. 4. Whole Health Experience: Patients expressed a desire for a more comfortable and supportive mammography process, greater access to care, enhanced trust between patients and providers, and comprehensive information sharing across healthcare providers. Education on breast cancer, early detection, and AI in healthcare was identified as a critical need. Conclusion: The findings underscore the importance of trustworthy, patient-centered approaches in healthcare, particularly in the integration of AI technologies. Enhancing patient education and provider transparency, improving access to preventive services, and maintaining the human element in healthcare are essential for optimizing patient outcomes and satisfaction. Citation Format: Nancy Brinker. Patient Experience and Perceptions Related to Breast Health, Mammography and Artificial Intelligence in Healthcare [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P3-05-11.

Background: Artificial Intelligence has rapidly emerged as a transformative force in healthcare, supporting advancements in clinical practice, diagnostics, treatment planning, and health system management. Its integration has improved accuracy, efficiency, and personalised patient care. However, alongside these advancements, significant challenges hinder its optimal utilization and widespread adoption. Objective: This review aims to examine the current utilization of AI in healthcare, identify key challenges associated with its development and implementation, and highlight emerging opportunities that can shape the future of AI-driven healthcare. Methodology: A narrative review methodology was adopted. Relevant peer-reviewed articles, reports, and policy documents published between 2014 and 2024 were retrieved from PubMed, Scopus, Web of Science, and Google Scholar. Keywords included “Artificial Intelligence in Healthcare,” “AI Applications,” “Healthcare Challenges,” and “Opportunities in AI.” Literature was screened for relevance and synthesised to provide an overview of current applications, challenges, and future opportunities. Results: AI is currently utilized in diagnostic imaging, predictive analytics, clinical decision support, telemedicine, robotic surgery, drug discovery, and personalised medicine. These applications contribute to faster diagnosis, improved treatment outcomes, and enhanced healthcare efficiency. However, adoption remains limited due to challenges such as data privacy concerns, algorithmic bias, high implementation costs, lack of clinician training, and insufficient regulatory clarity. Emerging opportunities include explainable AI, enhanced data governance, interdisciplinary collaboration, and increased access to AI-driven tools through scalable digital health solutions. Conclusion: AI holds substantial potential to advance healthcare delivery. Addressing existing challenges through ethical practices, strengthened regulations, capacity building, and equitable access will be essential for harnessing AI’s emerging opportunities to improve global health outcomes.