Artificial Intelligence within Healthcare

Guidance & General Resources on AI Concerns

The Trustworthy and Responsible AI Network (TRAIN), comprised of a consortium of healthcare leaders, is dedicated to ethical and equitable AI driven healthcare.  Along with member organizations, TRAIN develops, adopts, and shares best practices for responsible and ethical use of AI in healthcare.

The FUTURE-AI framework, developed by a consortium of interdisciplinary experts across the globe, provides guidance for the development and deployment of trustworthy AI tools in healthcare.

The WHO guidance on Ethics and Governance of Artificial Intelligence for Health, developed over eighteen months by experts in various fields, addresses the promise of AI in improving health related functions such as diagnosis, treatment, and public health response. The report emphasizes that AI technologies must prioritize ethics and human rights, ethical challenges, and six principles to ensure AI benefits all countries, and provides recommendations for governing AI. 

Currently, the FDA has established a framework for evaluating AI-based medical devices focusing on premarket review, post-market surveillance, and continuous learning.  AI models are assessed based on their intended use, risk to patients, and the nature of their algorithm. Diagnostic AI tools, which directly inform clinical decision without human intervention, carry a higher risk if they fail or provide inaccurate results. Consequently, they require comprehensive validation and monitoring to ensure their safety and effectiveness. See also, Artificial Intelligence in Software as a Medical Device.

Additional Resources:

Campus Resources:

Privacy

Privacy PubMed Search

  • Digitising health history: The creation, function and implementation of the Norwegian Health Archives RegistryThis link opens in a new window CONCLUSION: NHAR offers significant potential for interdisciplinary research across various medical fields.Implications for health information management practice:NHAR establishes a foundation for secure access to historical health data and introduces advanced data management strategies to facilitate future research. Nov 8, 2025
  • Innovative strategies for reconstructing medical education through technology: a literature reviewThis link opens in a new window Grounded in educational psychology, the landscape of medical education is experiencing a transformative evolution, catalyzed by the synergistic application of advanced technologies. This review synthesizes the burgeoning potential of neuroscientific research, big data analytics, mobile learning, virtual reality (VR), augmented reality (AR), social network analysis, natural language processing (NLP), physical activity monitoring, experimental economics, and adaptive learning technologies. These... Nov 6, 2025
  • Federated nnU-Net for privacy-preserving medical image segmentationThis link opens in a new window The nnU-Net framework has played a crucial role in medical image segmentation and has become the gold standard in multitudes of applications targeting different diseases, organs, and modalities. However, so far it has been used primarily in a centralized approach where the collected data is stored in the same location where nnU-Net is trained. This centralized approach has various limitations, such as potential leakage of sensitive patient information and violation of patient privacy. Federated... Nov 3, 2025
  • Quantum resilient security framework for privacy preserving AI in Apple MM1 on device architectureThis link opens in a new window The emergence of multi-modal models such as Apple’s MM1 signifies a transition towards on-device artificial intelligence, diminishing dependence on cloud inference. However, quantum developments render classical cryptography vulnerable to data breach. We present QSAFE-MM1, a quantum-resilient security architecture that incorporates Federated Learning (FL), Fully Homomorphic Encryption (FHE), and lattice-based cryptography to enhance MM1’s security. Federated Learning (FL) facilitates... Nov 3, 2025
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Additional Readings:

Ethical Conversations

Ethics PubMed Search

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Bias

Bias PubMed Search

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Reliability

Reliability PubMed Search

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Select Resources:

What is hallucination (in models like ChatGPT)? (UA Libraries AI FAQs)
How can I fact-check the information that ChatGPT and other language models give me? (UA Libraries AI FAQs)
What are guardrails (in tools like ChatGPT)? (UA Libraries AI FAQs)
I can’t find the citations that ChatGPT gave me. What should I do? (UA Libraries AI FAQs)