HealthPositive state · G 76 / P 69
Source article: Machine Learning-Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis
Problem
Pooled performance estimates were based predominantly on internal validation, with true external validation remaining sparse, limiting confidence in generalizability.
Brain and BehaviorGain
Machine learning models that integrate clinical and radiomics features achieved high pooled discrimination for predicting hematoma expansion, poor functional outcome, and mortality in adults with spontaneous intracerebral hemorrhage.
Brain and BehaviorHealthContested · G 73 / P 72
Source article: [Use of artificial intelligence in clinical practice and hospitals]
Problem
Clinical use of AI is limited by hallucinations, algorithmic bias, data protection requirements, and regulatory considerations that require continuous human oversight.
Die UrologieGain
AI systems are being used in urology and hospital care to support image interpretation, risk stratification, clinical decision-making, documentation, and workflow optimization.
Die UrologieHealthContested · G 69 / P 69
Source article: Development and external validation of a machine learning model for predicting postoperative hydrocephalus in 1,073 posterior fossa tumor patients
Problem
Calibration assessment showed dataset shift, so absolute predicted probabilities should be interpreted cautiously and the model should not drive CSF diversion decisions alone without prospective validation.
Neurosurgical ReviewGain
A preoperative model using Evans index, tumor-fourth ventricle relationship, and preoperative CSF diversion status achieved good external discrimination for postoperative hydrocephalus after posterior fossa tumor resection.
Neurosurgical ReviewHealthPositive state · G 70 / P 64
Source article: Innovative approaches to therapeutic target discovery amid the global challenge of antimicrobial resistance
Problem
AI-driven modeling for antimicrobial resistance target discovery continues to face persistent challenges around experimental validation and genomic variability, limiting confirmation of predicted functions.
Journal of Computer-Aided Molecular DesignGain
Structural modeling tools such as AlphaFold and RoseTTAFold enable high-accuracy 3D prediction of proteins encoded by multidrug-resistant bacterial genomes, facilitating functional annotation and accelerating design of new antimicrobial compounds.
Journal of Computer-Aided Molecular DesignClimateContested · 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
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