EducationNegative state · G 67 / P 75
Source article: The Role of AI in Nursing Education and Practice: Umbrella Review
Problem
AI adoption in nursing faces barriers including data privacy risks, algorithmic bias, lack of transparency and accountability, resistance to adoption, and disparities in access to AI technologies and standardized education.
Journal of Medical Internet ResearchHealthContested · G 71 / P 72
Source article: PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods
Problem
The original 2019 PROBAST tool had become outdated given rapid progress in prediction modelling methodology and AI including machine learning.
BMJGain
PROBAST+AI provides a unified tool that lets stakeholders assess quality, bias, and applicability of both regression and AI-based prediction models in healthcare.
BMJPolicyContested · G 65 / P 69
Source article: AI Moderation and Legal Frameworks in Child-Centric Social Media: A Case Study of Roblox
Problem
Current automated and human moderation systems on Roblox fail to prevent young users' exposure to inappropriate content, cyberbullying, and predatory behavior.
LawsGain
Hybrid AI-human moderation can improve efficient filtering of user-generated content on child-focused metaverse platforms like Roblox.
LawsHealthContested · G 71 / P 70
Source article: Machine learning in point-of-care testing: innovations, challenges, and opportunities
Problem
ML-enhanced point-of-care testing faces regulatory hurdles, reliability questions, and privacy concerns that limit widespread clinical adoption.
Nature CommunicationsGain
ML integration into point-of-care platforms improves diagnostic accuracy, sensitivity, and efficiency and can expand decentralized testing access.
Nature CommunicationsBusinessContested · G 68 / P 69
Source article: Integration of smart sensors and IOT in precision agriculture: trends, challenges and future prospectives
Problem
Deploying IoT-enabled smart sensors with AI for precision agriculture is limited by high initial investment costs, complexities in data management, requirements for technical expertise, data security and privacy concerns, and connectivity issues in remote agricultural areas.
Frontiers in Plant ScienceGain
Integration of sensor networks with AI and ML platforms enables real-time monitoring and predictive analytics for disease outbreaks and yield forecasting, allowing targeted irrigation, fertilization and pest management that optimizes resource use and improves sustainable farming efficiency.
Frontiers in Plant ScienceHealthContested · G 72 / P 69
Source article: Large Language Models in Medicine: Applications, Challenges, and Future Directions
Problem
Medical LLMs still face numerous challenges in practical applications, including hallucination, limited interpretability, and ethical concerns that hinder widespread use.
International Journal of Medical SciencesGain
Large language models represented by GPT-4 have been gradually implemented in clinical practice, medical research, and medical education with transformative potential for healthcare delivery.
International Journal of Medical SciencesHealthContested · G 71 / P 71
Source article: The evolving field of digital mental health: current evidence and implementation issues for smartphone apps, generative artificial intelligence, and virtual reality
Problem
Digital mental health tools are hampered by engagement challenges, industry setbacks, methodological critiques, and gaps in evidence and scaling that limit real-world applicability.
World PsychiatryGain
Smartphone apps, virtual reality, and generative AI including large language models show utility across well-being and clinical conditions and can positively impact mental health care when deployed as tools to augment and extend care.
World PsychiatryHealthContested · G 74 / P 71
Source article: A framework to assess clinical safety and hallucination rates of LLMs for medical text summarisation
Problem
LLM summarisation of consultations showed a 1.47% hallucination rate and 3.45% omission rate, creating fidelity gaps that could compromise patient safety.
npj Digital MedicineGain
Refining prompts and workflows within the proposed framework reduced major errors below previously reported human note-taking rates, supporting safer clinical documentation.
npj Digital MedicineCrimeContested · G 67 / P 69
Source article: Applications of AI-Based Models for Online Fraud Detection and Analysis
Problem
Fraud-detection models trained for specific scam types often fail to generalize to new fraud types and lose effectiveness when trained on outdated data, with inconsistent performance reporting.
Crime ScienceGain
AI and NLP models trained on text data can detect and analyze patterns across multiple categories of online fraud.
Crime ScienceHealthContested · G 73 / P 75
Source article: AI-Driven Wearable Bioelectronics in Digital Healthcare
Problem
AI-driven wearables face technical, ethical and regulatory hurdles including data interoperability, privacy concerns, algorithmic bias, scalability and security that limit widespread clinical adoption.
Journal of Electronic & Information SystemsGain
AI-driven wearable bioelectronics enable continuous monitoring of cardiac activity, glucose and biomarkers to support early disease detection, chronic disease management and remote patient monitoring.
BiosensorsPolicyNegative state · G 64 / P 69
Source article: When Is a Decision Automated? A Taxonomy for a Fundamental Rights Analysis
Problem
Current GDPR and AI Act definitions of automated decision-making fail to capture real-life applications where automated systems assist rather than replace human decision-makers in migration and asylum.
German Law JournalGain
A proposed taxonomy for automated decision-making could improve identification of fundamental rights at stake in public migration, asylum and mobility decisions.
German Law JournalScienceContested · G 69 / P 73
Source article: Harnessing data science and artificial intelligence to advance implementation research and practice
Problem
Application of AI to implementation faces persistent risks of insufficient or biased data, equity and access barriers, and concerns around data security, trust and ethics requiring oversight.
JBI Evidence ImplementationGain
Large language models, clustering and sentiment analysis demonstrated in a precision oncology project and integrated in ImpleMATE platform enable continuous knowledge extraction and decision support to improve implementation of evidence-based interventions in routine health care.
JBI Evidence ImplementationHealthContested · G 74 / P 72
Source article: Optimizing Treatment Strategies in the Bipolar Disorder Spectrum With Classical AI Approaches: Systematic Review of Performance, Bias, and Clinical Applicability
Problem
Most classical AI models for bipolar disorder treatment optimization had high risk of bias and lack of external validation, and remain exploratory rather than ready for clinical use.
JMIR Mental HealthGain
Systematic review of 35 studies found classical AI models for bipolar disorder achieved pooled AUC 0.80 for long-term maintenance response and 85%-97% accuracy for safety and dose optimization.
JMIR Mental HealthPolicyContested · G 69 / P 72
Source article: Artificial Intelligence Adoption in SMEs: Survey Based on TOE–DOI Framework, Primary Methodology and Challenges
Problem
Small and medium-sized enterprises continue to face significant challenges in effective AI adoption, with ten critical barriers across technology, organization, and environment including data access, skill shortages, cultural resistance, infrastructure limitations, and weak governance.
Applied SciencesGain
A structured six-phase roadmap methodology guides SMEs through AI adoption by pairing each barrier with actionable, context-sensitive solutions and incorporating responsible AI governance and open-weight LLMs.
Applied SciencesPolicyNegative state · G 70 / P 75
Source article: Evaluating Trustworthiness in AI: Risks, Metrics, and Applications Across Industries
Problem
AI systems face major risks across lifecycle stages including reliability failures and bias, and optimizing trustworthiness forces trade-offs such as fairness versus efficiency or privacy versus transparency.
ElectronicsGain
Systematic trustworthiness frameworks and metrics can guide building resilient, ethical and transparent AI systems and have been applied in case studies across healthcare, financial services and autonomous systems.
Electronics
ace
