Generative Artificial Intelligence (GenAI) is rapidly reshaping the creation, evaluation, and use of information across the health sciences. For health information professionals grounded in evidence-based practice, information stewardship, and ethical knowledge management, this shift presents both opportunities and responsibilities. As GenAI tools are integrated into instruction, evidence synthesis, clinical support, and research workflows, it is essential to articulate the values and competencies that guide responsible adoption.
This document highlights core ethical and sustainability considerations that are especially relevant for health information, where accuracy, reproducibility, and protection of sensitive data are foundational. The framework is organized around key considerations for evaluating and using GenAI, including bias, expertise, sustainability, transparency, privacy, and property, and how they intersect with and impact our roles as health information professionals. Rather than outlining prescriptive practices, the sections that follow focus on the skills and competencies health information professionals rely on to evaluate and use GenAI tools responsibly—and to help others do the same.
These considerations are tailored to the health sciences. Additional resources more broadly applicable to librarianship and education are provided below.
Bias
- Key Challenge: GenAI models may amplify existing biases in scholarly data and publications.
- Why it Matters in Health Information Settings: In health settings, amplified bias is critical because it translates skewed scholarly data into discriminatory clinical advice, directly risking misdiagnosis and inequitable care for marginalized populations. For health information professionals, this undermines the integrity of evidence-based practice, potentially turning AI-driven literature into a tool that reinforces systemic health disparities rather than solving them.
- Relevant MLA Competenciesand Indicators:
- Competency 1: Information Services
- Selects Information.
- Competency 4: Leadership and Management
- Integrates multicultural awareness and fairness into professional practice.
- Competency 5: Evidence-Based Practice and Research
- Finds and evaluates evidence to support decision-making.
- Conducts research.
- Competency 1: Information Services
- Competencies in Practice: As health information professionals select information, including AI-generated content, we should receive training and access to tools to assess its authority, provenance, and accuracy. We should also receive training and access to tools that help us recognize bias in AI-generated content and develop workflows to mitigate it.
Expertise
- Key Challenge: GenAI outputs may contain incorrect, incomplete, or misleading information. The technology is designed to generate a response that sounds plausible, although it may not be accurate.
- Why it Matters in Health Information Settings: In health information settings, the “plausible but inaccurate” nature of GenAI is a critical risk because unverified outputs can lead to fatal clinical errors or flawed health policies if used to guide patient care. Because these systems favor language fluency over factual accuracy, health information professionals must provide essential human oversight, using domain expertise to critically evaluate and verify AI-generated evidence against trusted sources to ensure the safety and integrity of medical decision-making.
- Relevant MLA Competencies and Indicators:
- Competency 1: Information Services
- Selects Information.
- Searches databases and other online resources.
- Competency 4: Leadership and Management
- Competency 5: Evidence-Based Practice and Research
- Finds and evaluates evidence to support decision making.
- Conducts research.
- Interprets data and presents statistical and data analyses.
- Competency 6: Health Information Professionalism
- Provides information and expert advice on current issues in health care information services.
- Contributes to the profession and shares expertise through publications, teaching, research, and service.
- Competencies In Practice: Health information professionals have a wide range of skills they can employ when using GenAI. Notably, prompt engineering represents a skill set closely aligned with established expertise in information retrieval. Additionally, critical appraisal is an essential part of selecting quality evidence, and health information professionals can adapt their existing instructional materials to include GenAI considerations.
Sustainability
- Key Challenge: As a technology, GenAI requires significant natural resources including water and electricity to power and cool data centers, rare earth metals to build the required hardware, and human resources to train and update the models most of whom are based in the global south.
- Why it Matters in Health Information Settings: In health information settings, the environmental and human cost of GenAI matters because the depletion of natural resources and exploitation of labor directly contribute to the global climate crisis and social inequities, both of which are fundamental determinants of human health. By advocating for sustainable development and the UN Sustainable Development Goals, health information professionals ensure that the tools used to improve healthcare do not simultaneously harm the ecosystems or vulnerable populations that those very health systems are designed to protect.
- Relevant MLA Competencies and Indicators:
- Competency 4: Leadership and Management
- Practices fiscal accountability and stewardship, and follows institutional resource policies.
- Competency 4: Leadership and Management
- Competencies In Practice: Health information professionals can recommend that GenAI is used only in situations where other existing technologies or human expertise would not perform at the same level. We can assess whether the environmental costs justify the anticipated benefits. We can advocate for greater transparency from vendors to disclose actual environmental impacts.
Transparency
- Key Challenge: Reproducibility is a foundational element of evidence syntheses and the scientific method in general. Transparency, explainability and traceability of GenAI outputs will help bring us closer to obtaining reproducible outcomes.
- Why it Matters in Health Information Settings: Given our role in evidence synthesis, we advocate for transparency and traceability in AI models from developers, vendors, and researchers using GenAI. This will help to ensure that AI outputs help to identify and synthesize the best evidence available according to the scientific method.
- Relevant MLA Competencies and Indicators:
- Competency 2: Information Management
- Conserves, preserves, and archives print and digital materials to maintain historical and scholarly records.
- Competency 2: Information Management
- Competencies in Practice: As users of GenAI, we should disclose when and how it was used and confirm that humans are ultimately responsible for all content that was AI-generated.
Privacy & Property
- Key Challenge: GenAI introduces significant challenges related to privacy, data protection, intellectual property, and the responsible handling of sensitive health information.
- Why it Matters in Health Information Settings: In health information settings, the risks to privacy and property are critical because any breach of sensitive patient data or proprietary research violates the fundamental legal and ethical foundations of the medical profession. This matters deeply to medical librarians because GenAI models often retain user inputs for training; if clinicians inadvertently input re-identifiable data, it risks the unauthorized disclosure of sensitive information and shatters the trust central to the patient-provider relationship. Furthermore, as stewards of scholarly information, librarians must navigate the complex boundaries of intellectual property and data sovereignty to ensure that AI integration does not exploit proprietary databases or undermine the attribution rights of original researchers.
- Relevant Competencies and Indicators:
- Competency 2: Information Management
- Adheres to copyright and intellectual property law.
- Competency 6: Health Information Professionalism
- Applies knowledge of the health care environment to respond to health care trends.
- Competency 2: Information Management
- Competencies in Practice: As health information professionals, we can provide guidance on how to train and use AI while respecting intellectual property rights of individuals, and when to refrain from inputting protected or regulated data into GenAI, taking into account institutional guidance, licenses, and private, enterprise-wide repositories.
Additional Resources
- Association of College and Research Libraries. ACRL’s AI competencies [Internet]. Chicago (IL): American Library Association; [cited 2026 Apr 7]. Available from: https://www.ala.org/acrl/standards/ai
- Association of Research Libraries. ARL AI resources [Internet]. Washington (DC): Association of Research Libraries; [cited 2026 Apr 7]. Available from: https://www.arl.org/artificial-intelligence/
- Committee on Publication Ethics (COPE). AI in scholarly publishing [Internet]. Eastleigh (UK): COPE; [cited 2026 Apr 7]. Available from: https://publicationethics.org/resources/discussion-documents/artificial-intelligence-ai-and-publication-ethics
- Educause. AI ethical guidelines [Internet]. Boulder (CO): Educause; 2025 Jun [cited 2026 Apr 7]. Available from: https://library.educause.edu/resources/2025/6/ai-ethical-guidelines
- Hosseini M, Gao P, Vivas-Valencia C. A social-environmental impact perspective of generative artificial intelligence. Environ Sci Ecotechnol [Internet]. 2024 Dec 15 [cited 2026 Apr 7];23:100520. Available from: https://www.sciencedirect.com/science/article/pii/S2666498424001340
- Hosseini M, Serge, Holmes K, Ross-Hellauer T. Open science at the generative AI turn: an exploratory analysis of challenges and opportunities. Quant Sci Stud. 2024 Nov 4:1–57.
- International Committee of Medical Journal Editors. Recommendations for the conduct, reporting, editing, and publication of scholarly work in medical journals (AI and authorship guidance updates) [Internet]. ICMJE; [cited 2026 Apr 7]. Available from: https://www.icmje.org
- National Academy of Medicine. An artificial intelligence code of conduct for health and medicine: essential guidance for aligned action [Internet]. Washington (DC): National Academies Press (US); 2025 [cited 2026 Apr 7]. Available from: https://www.nationalacademies.org/publications/29087
- National Academy of Medicine. Generative artificial intelligence in health and medicine: opportunities and responsibilities for transformative innovation. Elliott A, Krishnan S, Sarich T, et al., editors. Washington (DC): National Academies Press (US); 2025 May 16.
- National Institute of Standards and Technology. NIST AI risk management framework (AI RMF 1.0) [Internet]. Gaithersburg (MD): U.S. Department of Commerce; [cited 2026 Apr 7]. Available from: https://www.nist.gov/itl/ai-risk-management-framework
- Resnik DB, Hosseini M. Disclosing artificial intelligence use in scientific research and publication: when should disclosure be mandatory, optional, or unnecessary? Account Res. 2025 Mar 24:1–13.
- Resnik DB, Hosseini M. The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool. AI Ethics. 2024 May 27;5.
- Tribelhorn S. Ethical AI assessment tool [Internet]. San Diego (CA): San Diego State University Library; c2024 [cited 2026 Apr 7]. Available from: https://libguides.sdsu.edu/SDSULibrarySustainability/EthicalAIRubric Licensed under CC BY-NC-SA 4.0.
AI Acknowledgement Statement
The authors used Microsoft CoPilot to develop and revise some of the content in this document. All AI-generated and co-created content was reviewed, edited, and curated by human authors.
Task Force Members
- Marie Ascher
- Dianne Babski
- Kristi Holmes
- Sarah Jewell
- Melissa Rethlefsen
- Michelle Rodell
- Gabriel Rios, Chair
- Elizabeth Kellermeyer, MLA Board Liaison
- Nicki Mehall, MLA HQ