ClimateContested · G 70 / P 67
Source article: The Role of Artificial Intelligence in Revolutionizing Industrial Automation for Municipal Waste Management in Industry 4.0
ClimatePositive state · G 73 / P 67
Source article: Global genomic surveillance of β-lactam resistance in Escherichia coli across human, animal, and environmental reservoirs
Problem
The same gradient-boosted models showed poor agreement with observed MICs for ampicillin and piperacillin-tazobactam and struggled with beta-lactamase inhibitor combinations.
Infection, Genetics and EvolutionGain
Gradient-boosted machine learning predicted carbapenem MICs from resistance gene profiles with strong performance for imipenem and ertapenem, supporting AMR surveillance.
Infection, Genetics and EvolutionHealthContested · G 72 / P 74
Source article: Artificial intelligence and machine learning in pharmaceutical research and healthcare: Ethical challenges and a framework for responsible implementation
HealthPositive state · G 77 / P 71
Source article: Research and application of machine learning models based on multimodal big data for precise transfusion management in acute myeloid leukaemia
Problem
Deployment of multimodal machine learning for AML transfusion management is limited by data privacy protection, data standardisation across platforms, and model interpretability for clinical adoption.
Transfusion MedicineGain
Machine learning models integrating multimodal big data improved precision transfusion management for AML patients by predicting transfusion demand and assessing transfusion reaction risks.
Transfusion MedicineHealthContested · G 69 / P 71
Source article: Machine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-<sup>123</sup>I-Ioflupane SPECT Imaging in a Real-World Parkinson's Disease Dataset
Problem
SPECT imaging features added limited prognostic value and implementing models on real-world data did not significantly close the gap between prognostic modeling and clinical implementation.
Movement Disorders Clinical PracticeGain
Random Forest and Gradient Boosting models trained on routinely collected clinical records predicted Hoehn and Yahr scores 5 years after ioflupane SPECT imaging, with best accuracy using 2 years of follow-up data.
Movement Disorders Clinical PracticeEducationContested · G 70 / P 70
Source article: Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education
HealthContested · G 66 / P 66
Source article: Ultra-widefield color fundus images and artificial intelligence for diagnosis of diabetic retinopathy: A systematic review and meta-analysis
Problem
AI-driven diabetic retinopathy screening using ultra-widefield fundus images showed limited specificity of 72.5% in meta-analysis.
RetinaGain
AI-driven diabetic retinopathy screening using ultra-widefield fundus images achieved a summary sensitivity of 85.0% and AUC of 0.870 in meta-analysis.
RetinaScienceContested · G 70 / P 69
Source article: Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations
Problem
Without careful dissection of how biases are encoded into AI health technologies, underlying health dataset limitations risk perpetuating existing health inequalities at scale.
The Lancet Digital HealthGain
STANDING Together consensus recommendations provide guidance for documenting health datasets and for using them to identify and mitigate algorithmic biases, aiming to support AI health technologies that are safe and effective across population groups.
The Lancet Digital HealthHealthContested · G 73 / P 71
Source article: Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration
Problem
Federated learning deployments in smart healthcare remain vulnerable to adversarial attacks, data poisoning, and model inversion, plus practical barriers of heterogeneous data, scalability, and system interoperability.
HealthcareGain
Federated learning allows hospitals and health systems to train shared models without centralizing patient data, supporting real-time IoT and wearable monitoring for predictive analytics and personalized care.
HealthcareEducationContested · G 72 / P 69
Source article: Generative AI in higher education: A global perspective of institutional adoption policies and guidelines
PolicyContested · G 68 / P 67
Source article: Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance
EducationContested · G 72 / P 70
Source article: Generative AI in Higher Education: Balancing Innovation and Integrity
Problem
Generative AI integration in higher education threatens academic integrity and equity by undermining authenticity of student work and widening inequalities.
British Journal of Biomedical ScienceHealthContested · G 68 / P 70
Source article: The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency
Problem
Mainstream implementation of AI in healthcare is hindered by data security issues and budget and resource constraints.
Health Science ReportsGain
AI integration in healthcare is enhancing medical professionals' diagnostic capabilities, enabling more individualized treatment plans, and improving operational effectiveness and patient involvement through applications like remote monitoring and predictive analytics.
Health Science ReportsClimateContested · G 72 / P 74
Source article: Artificial intelligence for modeling and understanding extreme weather and climate events
Problem
AI for extreme climate events is limited by noisy, heterogeneous, small sample sizes with limited annotations, challenges integrating real-time information, and lack of understandable models needed for stakeholder trust and regulatory compliance.
Nature CommunicationsGain
AI models are improving forecasting and analysis of extreme climate events such as floods, droughts, wildfires and heatwaves, helping to enhance disaster response and risk communication.
Nature CommunicationsEducationContested · G 72 / P 73
Source article: Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy
Problem
The same AI-driven neuroadaptive learning systems raise implementation problems for K-12 and adult learners, including data privacy and data security risks, ethical concerns and algorithmic bias, scalability issues, and accessibility disparities.
Brain SciencesGain
AI-driven adaptive learning systems informed by EEG, fNIRS and other neurophysiological data improved learning efficacy for K-12 students and adult learners by automatically managing cognitive load and dynamically personalizing instruction and feedback.
Brain Sciences
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