ethique_IA

Ethical aspects and user preferences in applying machine learning to adjust eHealth addressing substance use: A mixed-methods study.

Tue, 01 Jul 2025 04:00:00 +0000

International Journal of Medical Informatics; 07/01/2025
(AN 184650966); ISSN: 13865056
CINAHL Complete

Artificial intelligence in melanoma diagnosis: ethical considerations and clinical implementation.

Tue, 01 Jul 2025 04:00:00 +0000

Baylor University Medical Center Proceedings; 07/01/2025
The use of artificial intelligence (AI) in dermatology, particularly for the diagnosis of melanoma, has demonstrated potential in improving early detection of cancer. Current AI-based systems, such as DermaSensor and Nevisense, have shown high sensitivity. In addition, open-source models like All Data Are Ext (ADAE) continue to show promise. Ethical, practical, and privacy concerns remain despite these advancements. Key challenges with these models include maintaining transparency with patients, ensuring privacy of patient data, and addressing discrepancies between AI and clinical determinations. Additional research, regulatory guidance, and open conversations are necessary to realize AI's full potential in the field of dermatology while preserving patient trust.
(AN 186012535); ISSN: 08998280
CINAHL Complete

Ethical Use of Artificial Intelligence (AI) in Scholarly Writing.

Tue, 01 Jul 2025 04:00:00 +0000

Journal of Pediatric Surgical Nursing; 07/01/2025
The advancement of generative artificial intelligence has sparked widespread discussion regarding its ethical implications in scholarly writing. While AI offers remarkable capabilities, such as assisting with research, drafting, and editing, it also raises concerns related to plagiarism, authorship, accuracy, transparency, and privacy. This review explores the ethical considerations surrounding AI use in academia, examining policies implemented by universities, publishers, and professional organizations. The paper outlines a structured approach to integrating AI responsibly in scholarly work, emphasizing five key stages: acceptance, seeking guidance, verifying accuracy, optimizing prompt engineering, and implementing ethical practices. Additionally, the review discusses recent guidelines from organizations such as the American Psychological Association and the International Committee of Medical Journal Editors.
(AN 186014951); ISSN: 23320249
CINAHL Complete

Strategic Guidelines to Integrate Artificial Intelligence in Obstetrics and Gynecology: Best Practices and Ethical Considerations

Wed, 25 Jun 2025 08:28:04 +0000

Author Names: Sengupta P.,Dutta S.,Bagchi S.,Hegde P.,Sebastian S.,Taylor D.C.M.,Henkel R.

Database Source: Embase Daily Updates

Journal Title: Reproductive Sciences

Article Title: Strategic Guidelines to Integrate Artificial Intelligence in Obstetrics and Gynecology: Best Practices and Ethical Considerations

Year: 2025

Issue:

Volume:

Abstract: The transformative advancements in artificial intelligence (AI) have significantly impacted medical fields, particularly obstetrics and gynecology (OBGYN). This manuscript presents a comprehensive set of twelve strategic guidelines for the effective integration of AI into OBGYN, emphasizing both best practices and ethical considerations. These guidelines cover essential domains including multidisciplinary collaboration, safeguarding patient safety and privacy, continuous staff training, mitigating algorithmic bias, promoting transparent communication with patients, and fostering a continuous feedback loop between clinicians and AI developers. Additional recommendations highlight the importance of staying updated on AI advancements, defining the role of AI within clinical governance frameworks, ensuring long-term sustainability, collaborating with AI vendors for customized solutions, and evaluating outcomes to inform future practice. These strategies are designed to position AI as an augmentative tool in clinical decision-making, ensuring it enhances rather than replaces human expertise. By upholding collaborative efforts and ethical standards, AI can profoundly improve care quality in OBGYN, fostering safer and more effective healthcare delivery.<br/>Copyright © The Author(s), under exclusive licence to Society for Reproductive Investigation 2025.

Applying an Agile Science Roadmap to Integrate and Evaluate Ethical Frameworks Throughout the Lifecycle and Use of Artificial Intelligence Tools in the Intensive Care Unit.

Sun, 01 Jun 2025 04:00:00 +0000

Critical Care Nursing Clinics of North America; 06/01/2025
(AN 185201952); ISSN: 08995885
CINAHL Complete

Predicting postoperative chronic opioid use with fair machine learning models integrating multi-modal data sources: a demonstration of ethical machine learning in healthcare.

Sun, 01 Jun 2025 04:00:00 +0000

Journal of the American Medical Informatics Association; 06/01/2025
(AN 185284489); ISSN: 10675027
CINAHL Complete

Development and Validation of a Scale for Nurses' Ethical Awareness in The Use of Artificial Intelligence: A Methodological Study.

Sun, 01 Jun 2025 04:00:00 +0000

Nursing & Health Sciences; 06/01/2025
The integration of artificial intelligence in nursing practice presents significant ethical challenges that require a comprehensive assessment framework. This study aimed to develop and validate a scale to measure nurses' ethical awareness regarding the use of artificial intelligence in clinical settings. A two‐phase methodological approach was employed, involving literature review and interviews for item generation, followed by psychometric evaluation. Data were collected using hand‐delivered questionnaires from a convenience sample of 650 nurses. After excluding outliers, 646 responses were randomly split for exploratory and confirmatory factor analyses. An initial pool of 36 items was refined to 21 items across six dimensions: data privacy and confidentiality, consent and autonomy, transparency and accountability, bias and equity, safety and professional integrity, and education and sustainability. Exploratory factor analysis identified a six‐factor structure explaining 71.5% of the variance, which was confirmed by confirmatory factor analysis with strong model fit indices. The scale demonstrated excellent reliability (Cronbach's alpha = 0.90) and satisfactory validity. These findings indicate the scale is a reliable and valid tool to assess nurses' ethical awareness in the use of artificial intelligence.
(AN 185939128); ISSN: 14410745
CINAHL Complete

The ethical dimensions of utilizing Artificial Intelligence in palliative care.

Sun, 01 Jun 2025 04:00:00 +0000

Nursing Ethics; 06/01/2025
Palliative care aims to improve the quality of life for seriously ill individuals and their caregivers by addressing their holistic care needs through a person- and family-centered approach. While there have been growing efforts to integrate Artificial Intelligence (AI) into palliative care practice and research, it remains unclear whether the use of AI can facilitate the goals of palliative care. In this paper, we present three hypothetical case examples of using AI in the palliative care context, covering machine learning algorithms that predict patient mortality, natural language processing models that detect psychological symptoms, and AI chatbots addressing caregivers' unmet needs. Using these cases, we examine the ethical dimensions of utilizing AI in palliative care by applying five widely accepted moral principles that guide ethical deliberations in AI: beneficence, nonmaleficence, autonomy, justice, and explicability. We address key ethical questions arising from these five core moral principles and analyze the potential impact the use of AI can have on palliative care stakeholders. Applying a critical lens, we assess whether AI can facilitate the primary aim of palliative care to support seriously ill individuals and their families. We conclude by discussing the gaps that need to be further addressed in order to promote ethical and responsible AI usage in palliative care.
(AN 185986000); ISSN: 09697330
Health Business Elite

Ethical and social considerations of applying artificial intelligence in healthcare—a two-pronged scoping review.

Tue, 27 May 2025 04:00:00 +0000

BMC Medical Ethics; 05/27/2025
(AN 185423223); ISSN: 14726939
CINAHL Complete

Artificial Intelligence in Health Care: A Rallying Cry for Critical Clinical Research and Ethical Thinking.

Thu, 01 May 2025 04:00:00 +0000

Clinical Oncology; 05/01/2025
Artificial intelligence (AI) will impact a large proportion of jobs in the short to medium term, especially in the developed countries. The consequences will be felt across many sectors including health care, a critical sector for implementation of AI tools because glitches in algorithms or biases in training datasets may lead to suboptimal treatment that may negatively affect the health of an individual. The stakes are obviously higher in case of potentially life-threatening diseases such as cancer and therapies with a potential for causing severe or even fatal adverse events. Over the last two decades, much of the research on AI in health care has focussed on diagnostic radiology and digital pathology, but a solid body of research is emerging on AI tools in the radiation oncology workflow. Many of these applications are relatively uncontroversial, although there is still a lack of evidence regarding effectiveness rather than efficiency, and—the ultimate bar—evidence of clinical utility. Proponents of AI will argue that these algorithms should be implemented with robust human supervision. One challenge here is the deskilling effect associated with new technologies. We will become increasingly dependent on the AI tools over time, and we will become less capable of assessing the quality of the AI output. Much of this research appears almost old-fashioned in view of the rapid advances in Generative artificial intelligence (GenAI). GenAI can draw from multiple types of data and produce output that is personalised and appears relevant in the given context. Especially the rapid progress in large language models (LLMs) has opened a wide field of potential applications that were out of bounds just a few years ago. One LLM, Generative Pre-trained Transformer 4 (GPT-4), has been made widely accessible to end-users as ChatGPT-4, which passed a rigorous Turing test in a recent study. In this viewpoint, I argue for the necessity of independent academic research to establish evidence-based applications of AI in medicine. Algorithmic medicine is an intervention similar to a new drug or a new medical device. We should be especially concerned about under-represented minorities and rare/atypical clinical cases that may drown in the petabyte-sized training sets. A huge educational push is needed to ensure that the end-users of AI in health care understand the strengths and weaknesses of algorithmic medicine. Finally, we need to address the ethical boundaries for where and when GenAI can replace humans in the relation between patients and healthcare providers.
(AN 184523221); ISSN: 09366555
CINAHL Complete

Why do we need to employ exemplars in moral education? Insights from recent advances in research on artificial intelligence.

Thu, 01 May 2025 04:00:00 +0000

Ethics & Behavior; 05/01/2025
In this paper, I examine why moral exemplars are useful and even necessary in moral education despite several critiques. To support my point, I review recent AI research demonstrating that exemplar-based learning is superior to rule-based learning in model performance in training neural networks, such as large language models. I particularly focus on why education aiming at promoting the development of multifaceted moral functioning can be done effectively by using exemplars, which is like exemplar-based learning in AI model training. Furthermore, I discuss the potential limitations and issues related to exemplar-applied moral education with findings from recent AI research raising concerns about model biases and toxic outcomes. I propose ways to address the concerns regarding employing moral exemplars as well. As remedies, I suggest that autonomy-supporting deliberative and reflective learning should be utilized. Furthermore, based on the discussion, I examine how macroscopic socio-cultural aspects influence the effectiveness of exemplar-applied education.
(AN 184864446); ISSN: 10508422
CINAHL Complete

Response to: "Ethical use of Artificial Intelligence in Health Professions Education: AMEE Guide No. 158"...Masters K. Medical Teacher. 2023;45(6):574-584.

Thu, 01 May 2025 04:00:00 +0000

Medical Teacher; 05/01/2025
(AN 184864327); ISSN: 0142159X
CINAHL Complete

Artificial intelligence-assisted academic writing: recommendations for ethical use.

Fri, 18 Apr 2025 04:00:00 +0000

Advances in Simulation; 04/18/2025
(AN 184604994); ISSN: 20590628
CINAHL Complete

The doctor and patient of tomorrow: exploring the intersection of artificial intelligence, preventive medicine, and ethical challenges in future healthcare.

Fri, 18 Apr 2025 04:00:00 +0000

Frontiers in Digital Health; 04/18/2025
(AN 184605572)
CINAHL Complete

Ethical implications related to processing of personal data and artificial intelligence in humanitarian crises: a scoping review.

Tue, 15 Apr 2025 04:00:00 +0000

BMC Medical Ethics; 04/15/2025
(AN 184470275); ISSN: 14726939
CINAHL Complete

Ethical Artificial Intelligence in Nursing Workforce Management and Policymaking: Bridging Philosophy and Practice.

Wed, 09 Apr 2025 04:00:00 +0000

Journal of Nursing Management; 04/09/2025
Background: Despite artificial intelligence's (AI) transformative potential in healthcare, nursing workforce scholarship lacks a cohesive theoretical foundation and well‐established philosophical stances to guide safe yet ethical, effective yet efficient, and sustainable AI integration into nursing workforce management and policymaking. This gap poses significant challenges in leveraging AI's benefits while mitigating potential risks and inequities. Aim: This paper aims to (1) present a philosophical discourse centered on Park's optimized nurse staffing (Sweet Spot) theory and (2) propose a novel theoretical framework with specific methodologies for ethical AI‐equipped nursing workforce management and policymaking while providing its philosophical underpinnings. Method: A rigorous philosophical discourse was performed through theoretical triangulation, grounded in Park's Optimized Nursing Staffing (Sweet Spot) Estimation Theory. This approach synthesizes diverse philosophical perspectives to create a robust foundation for ethical AI integration in nursing workforce management and policymaking. Discussion: The novel theoretical framework introduces its well‐established philosophical underpinnings, bridging moderate realism with post-positivism and contextualism, for ethical AI‐equipped nursing workforce management and policymaking. The framework also provides practical solutions for ethical AI integration while ensuring equity and fairness in nursing workforce practices. This approach consequently offers a groundbreaking pathway toward sustainable AI‐equipped nursing workforce management and policymaking that balances safety, ethics, effectiveness, and efficiency. Implication on Nursing Management: This paper is the first to present a theoretical framework for ethically integrating AI into nursing workforce management and policymaking, grounded in its robust philosophical underpinnings. It stands out for its creativity and originality, making a significant contribution by opening new avenues for emerging research and development at the intersection of AI and healthcare. Specifically, the framework serves as a practical and pivotal resource for researchers, policymakers, and healthcare administrators navigating the complex landscape of AI integration in nursing workforce management and policymaking. Above all, it is worthwhile in that this paper contributes to the broader intellectual discourse in a thought‐provoking and timely manner by addressing AI's inherent limitations in healthcare through a theoretical framework embedded in human philosophical and ethical deliberation. Unlike the current practice where AI safety and ethical risk assessment are conducted after AI solutions have been developed, this approach provides proactive guidance. Thereby, it lays the crucial groundwork for future empirical studies and practical implementations toward desirable healthcare decision‐making.
(AN 184339341); ISSN: 09660429
CINAHL Complete

Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice.

Tue, 08 Apr 2025 04:00:00 +0000

PLoS Digital Health; 04/08/2025
(AN 184322646); ISSN: 27673170
CINAHL Complete

The Ethical Concerns of Artificial Intelligence in Urban Planning.

Tue, 01 Apr 2025 04:00:00 +0000

Journal of the American Planning Association; 04/01/2025
Problem, research strategy, and findings: The integration of a artificial intelligence (AI) into urban planning presents potential ethical challenges, including concerns about bias, transparency, accountability, privacy, and misinformation. As planners rely more on AI for decision making, the potential for these systems to perpetuate biases, obscure decision-making processes, and infringe on privacy becomes more pronounced, potentially undermining public trust and excluding marginalized communities. We reviewed existing literature on AI ethics in urban planning, examining biases, transparency, accountability, and privacy issues. Our methodology synthesized findings from various studies, reports, and theoretical frameworks to highlight ethical concerns in AI-driven urban planning. Recommendations for ethical AI implementation emphasize transparency, inclusive data sets, public engagement, and robust ethical guidelines. Our research identified critical ethical concerns in AI-driven urban planning. Bias in AI systems can lead to unequal outcomes, disproportionately affecting marginalized communities. Transparency issues arise from the black box nature of AI, complicating understanding and trust in AI-driven decisions. Privacy concerns are heightened due to extensive data collection and potential misuse, raising the risk of surveillance and data breaches. Limitations include the availability of specific literature focused on AI ethics for urban planning and the evolving nature of AI technologies, suggesting a need for ongoing research and adaptive strategies. Human oversight and continuous monitoring are essential to ensure ethical practices, with an emphasis on community engagement and public education to foster trust and inclusivity. Takeaway for practice: Urban planners should adopt a proactive approach to mitigate ethical risks associated with AI. Ensuring transparency, involving diverse community groups, and maintaining robust data privacy measures are crucial. Prioritizing public engagement and education will help to demystify AI technologies and build public trust. Addressing these ethical concerns allows planners to leverage AI's potential while safeguarding equity, privacy, and accountability in urban development.
(AN 183596736); ISSN: 01944363
Health Business Elite

Ethical Application of Generative Artificial Intelligence in Medicine.

Tue, 01 Apr 2025 04:00:00 +0000

Arthroscopy: The Journal of Arthroscopy & Related Surgery; 04/01/2025
(AN 183760354); ISSN: 07498063
CINAHL Complete

Integrating artificial intelligence ethically in nursing education.

Tue, 01 Apr 2025 04:00:00 +0000

Nursing; 04/01/2025
(AN 183979058); ISSN: 03604039
CINAHL Complete

A systematic literature review on artificial intelligence in recruiting and selection: a matter of ethics.

Tue, 01 Apr 2025 04:00:00 +0000

Personnel Review; 04/01/2025
(AN 184272681); ISSN: 00483486
Health Business Elite

Con: Artificial Intelligence in Manuscript Writing: Pitfalls and Ethical Concerns the Authors Should Be Aware.

Tue, 01 Apr 2025 04:00:00 +0000

Annals of Cardiac Anaesthesia; 04/01/2025
(AN 184527014); ISSN: 09719784
CINAHL Complete

Artificial Intelligence in Medical Care – Patients' Perceptions on Caregiving Relationships and Ethics: A Qualitative Study.

Tue, 01 Apr 2025 04:00:00 +0000

Health Expectations; 04/01/2025
Introduction: Artificial intelligence (AI) offers several opportunities to enhance medical care, but practical application is limited. Consideration of patient needs is essential for the successful implementation of AI‐based systems. Few studies have explored patients' perceptions, especially in Germany, resulting in insufficient exploration of perspectives of outpatients, older patients and patients with chronic diseases. We aimed to explore how patients perceive AI in medical care, focusing on relationships to physicians and ethical aspects. Methods: We conducted a qualitative study with six semi‐structured focus groups from June 2022 to March 2023. We analysed data using a content analysis approach by systemising the textual material via a coding system. Participants were mostly recruited from outpatient settings in the regions of Halle and Erlangen, Germany. They were enrolled primarily through convenience sampling supplemented by purposive sampling. Results: Patients (N = 35; 13 females, 22 males) with a median age of 50 years participated. Participants were mixed in socioeconomic status and affinity for new technology. Most had chronic diseases. Perceived main advantages of AI were its efficient and flawless functioning, its ability to process and provide large data volume, and increased patient safety. Major perceived disadvantages were impersonality, potential data security issues, and fear of errors based on medical staff relying too much on AI. A dominant theme was that human interaction, personal conversation, and understanding of emotions cannot be replaced by AI. Participants emphasised the need to involve everyone in the informing process about AI. Most considered physicians as responsible for decisions resulting from AI applications. Transparency of data use and data protection were other important points. Conclusions: Patients could generally imagine AI as support in medical care if its usage is focused on patient well‐being and the human relationship is maintained. Including patients' needs in the development of AI and adequate communication about AI systems are essential for successful implementation in practice. Patient or Public Contribution: Patients' perceptions as participants in this study were crucial. Further, patients assessed the presentation and comprehensibility of the research material during a pretest, and recommended adaptations were implemented. After each FG, space was provided for requesting modifications and discussion.
(AN 184679857); ISSN: 13696513
CINAHL Complete

OCCUPATIONAL PARTICIPATION WITH TECHNOLOGY AND ARTIFICIAL INTELLIGENCE: CONNECTING ETHICS AND OPPORTUNITIES.

Tue, 01 Apr 2025 04:00:00 +0000

Occupational Therapy Now; 04/01/2025
(AN 185336073); ISSN: 14815532
CINAHL Complete

On the ethical and moral dimensions of using artificial intelligence for evidence synthesis.

Wed, 19 Mar 2025 04:00:00 +0000

PLoS Global Public Health; 03/19/2025
(AN 183843737); ISSN: 27673375
CINAHL Complete

What makes clinical machine learning fair? A practical ethics framework.

Tue, 18 Mar 2025 04:00:00 +0000

PLoS Digital Health; 03/18/2025
(AN 183819017); ISSN: 27673170
CINAHL Complete

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Ethics in Patient Preferences for Artificial Intelligence–Drafted Responses to Electronic Messages.

Tue, 11 Mar 2025 04:00:00 +0000

JAMA Network Open; 03/11/2025
Key Points: Question: How do patients feel about the use of artificial intelligence (AI) to draft responses in patient-clinician portal messages? Findings: This survey study of 1455 respondents showed that while overall satisfaction was high (>75%) regardless of author, respondents preferred responses written by AI over those written by a human (mean difference, 0.30 points on a 5-point Likert scale for satisfaction). However, when an AI author was disclosed, satisfaction was lower for AI compared with a human author (mean difference, 0.13 points). Meaning: Reduced satisfaction due to AI disclosure should be balanced with the importance of patient autonomy and empowerment. This survey study analyzes patient preferences around the use of artificial intelligence vs humans in generating responses to patient portal messages. Importance: The rise of patient messages sent to clinicians via a patient portal has directly led to physician burnout and dissatisfaction, prompting uptake of artificial intelligence (AI) to alleviate this burden. It is important to understand patient preferences around AI in patient-clinician communication as ethical guidelines on appropriate use and disclosure (patient notification of AI use) are developed. Objective: To analyze patient preferences regarding use of AI in electronic messages. Design, Setting, and Participants: A survey study was conducted within the Duke University Health System's patient advisory committee, consisting of individuals 18 years or older who participate in periodic surveys to inform health system patient care practices. Multiple surveys were administered to test the impact of different factors, including response author, disclosure (AI, human, or none), and seriousness of the topic. A follow-up survey assessed preferred disclosure verbiage. Surveys were administered from October 31 to December 11, 2023. Exposure: Multiple surveys. Main Outcomes and Measures: Participants rated their overall satisfaction, usefulness of the information, and perceived level of care on a 5-point Likert scale. Results: Of the 2511 members surveyed, 1455 (57.9%) responded, with respondents being older (median age, 57 [IQR, 49-70] vs 53 [IQR, 41-62] years), more educated (872 of 1083 [80.5%] vs 319 of 440 [72.5%] with a college or graduate degree), and predominantly female (921 [63.3%]). Participants preferred AI- compared with human-drafted responses, with a mean difference for satisfaction of −0.30 (95% CI, −0.37 to −0.23) points, usefulness of −0.28 (95% CI, −0.34 to −0.22) points, and perception they were cared for of −0.43 (95% CI, −0.50 to −0.37) points. Participants tended to have higher satisfaction with a human disclosure over AI disclosure, with a mean difference of 0.13 (95% CI, 0.05-0.22) points, and with no disclosure over AI authorship disclosure, with a mean difference of 0.09 (95% CI, 0.01-0.17) points. Regardless of author or disclosure type, more than 75% of respondents were satisfied (agree or strongly agree) with the response. Conclusions and Relevance: In this survey study, participants expressed a mild preference for messages written by AI but had a slightly decreased satisfaction when told AI was involved. Patient experience must be considered along with ethical implementation of AI. Although AI disclosure may slightly reduce satisfaction, disclosure should be maintained to uphold patient autonomy and empowerment.
(AN 183692168); ISSN: 25743805
CINAHL Complete

Integrating Artificial Intelligence Support in Patient Care While Respecting Ethical Principles.

Tue, 11 Mar 2025 04:00:00 +0000

JAMA Network Open; 03/11/2025
(AN 183692163); ISSN: 25743805
CINAHL Complete

The ethics of non-explainable artificial intelligence: an overview for clinical nurses.

Thu, 06 Mar 2025 05:00:00 +0000

British Journal of Nursing; 03/06/2025
Artificial intelligence (AI) is transforming healthcare by enhancing clinical decision-making, particularly in nursing, where it supports tasks such as diagnostics, risk assessments, and care planning. However, the integration of non-explainable AI (NXAI) – which operates without fully transparent, interpretable mechanisms – presents ethical challenges related to accountability, autonomy, and trust. While explainable AI (XAI) aligns well with nursing's bioethical principles by fostering transparency and patient trust, NXAI's complexity offers distinct advantages in predictive accuracy and efficiency. This article explores the ethical tensions between XAI and NXAI in nursing, advocating a balanced approach that emphasises outcome validation, shared accountability, and clear communication with patients. By focusing on patient-centred, ethically sound frameworks, it is argued that nurses can integrate NXAI into practice, addressing challenges and preserving core nursing values in a rapidly evolving digital landscape.
(AN 183570848); ISSN: 09660461
CINAHL Complete

On the practical, ethical, and legal necessity of clinical Artificial Intelligence explainability: an examination of key arguments.

Wed, 05 Mar 2025 05:00:00 +0000

BMC Medical Informatics & Decision Making; 03/05/2025
(AN 183454765); ISSN: 14726947
CINAHL Complete

Ethical considerations in patient-directed artificial intelligence platforms.