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Users reported AI limitations, lack of cohesive and predictable interactions, and user interface issues that detracted from experience with Wysa.

Applying GnAI to literary fiction editing raises unresolved ethical and industrial concerns about intellectual property, trust, the author-editor relationship, and whether core professional editing principles translate to AI.

Writers who used LLM-generated story ideas produced short stories that were more similar to each other, reducing collective diversity and producing a narrower scope of novel content.

Complexity and volume of wearable sensor data create substantial modeling challenges, with additional barriers of data quality, computational requirements, interpretability, and privacy concerns for LLM deployment.

AI-integrated microfluidic technologies face persistent challenges in manufacturing, clinical validation, and system integration that limit translation into routine clinical and public health practice.

Human-in-the-loop AI faces persistent concerns about algorithmic accountability, interpretability, and safety, plus challenges with workflow integration and regulatory gaps for adaptive systems.

Those TFP gains are likely exaggerated and even more modest, predicted to be less than 0.53% over 10 years, because future AI effects will involve hard-to-learn tasks with many context-dependent factors and no objective outcome measures.

Misuse of LLMs in manuscript preparation and peer review risks hallucinations, confidentiality breaches, erosion of critical skills, and proliferation of low-quality manuscripts.

AI models in geoscience are hindered by data scarcity, computational demands, data privacy concerns, and black-box opacity, while traditional physics models struggle to capture Earth's complexities.

Synthetic audio-visual deepfakes are proliferating across digital life, creating governance challenges for policymakers and societies.

Widespread use of ChatGPT and other generative AI has raised potential ethical issues in high-stakes health care applications, but ethical discussions have not yet been translated into operationalisable solutions.

The EU AI Act's horizontal approach insufficiently addresses patient interests in healthcare, leaving gaps that require additional sector-specific guidelines.

Generative AI in education brings limitations, potential disruptions, ethical consequences, and risks of misuse if adopted hastily without efficacy and ethics review.

AI-enabled nanomedicine development faces persistent challenges with data quality, interpretability, and generalizability that hinder reproducible synthesis and reliable clinical translation.

When deployed in criminal justice, social welfare, fraud detection and public health, standard ML models can produce unreliable and harmful predictions due to misalignment with public-sector realities.