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Problem Space a ranking of the record

TheProblems surrounding AI

334 documented problems are on the board. 53% rest on at least one peer-reviewed study, and 5% clear the high-evidence bar. Health carries 31% of it. The latest was filed Oct 12, 2026.

Every problem on the board, one square each, shaded by the strength of its evidence. High 18 Moderate 316 Emerging 0
334 results
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The board · page 2 of 12

31
Evidence-backed problemPeer-reviewedHealth

Imaging subtypes reveal distinct biological substrates and disability profiles in multiple sclerosis: Cognitive performance and genetic risk scores for multiple sclerosis severity did not differ across subtypes, but within each subtype advancing SuStaIn stage was associated with worse global and domain-specific cognitive performance (all p≤0.030).

Source article: Imaging subtypes reveal distinct biological substrates and disability profiles in multiple sclerosis

Brain
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32
Evidence-backed problemPeer-reviewedHealth

The growing integration of artificial intelligence (AI) in healthcare introduces new possibilities for enhancing clinical decision-making.ObjectiveThis study compared the performance of AI and perioperative nurses with varying experience levels in identifying errors during the preoperative patient transfer process.MethodsA controlled simulation study was conducted involving three nurses (novice, intermediate, and expert) and a ChatGPT-4o AI model.

Source article: Artificial intelligence and perioperative nurses in error detection during operating room transfers: A simulation-based comparison

WORK: A Journal of Prevention, Assessment & Rehabilitation
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33
Evidence-backed problemPeer-reviewedHealth

While agentic AI raises familiar moral concerns regarding safety, accountability and bias, this article focuses on a less explored dimension: its capacity to transform the moral fabric of healthcare itself.

Source article: Agentic AI, medical morality and the transformation of the patient-physician relationship

Journal of Medical Ethics
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34
Evidence-backed problemPeer-reviewedHealth

Artificial intelligence (AI) is expanding the capacity of public health systems to detect infectious disease signals, forecast outbreaks, analyse pathogen evolution, generate localised risk estimates, and support operational decisions.

Source article: Leveraging AI for infectious disease modelling and public health decision making

BMC Proceedings
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35
Evidence-backed problemPeer-reviewedHealth

Static and dynamic features derived from these indices were compared between groups and used to train machine learning models for PD-HC discrimination, with each model trained and evaluated separately within each cohort.

Source article: Quantitative Assessment of Facial Expression Asymmetry in Parkinson's Disease

Journal of Movement Disorders
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36
Evidence-backed problemPeer-reviewedScience

The most frequently reported challenges involved bias, errors, and limitations. Background Generative artificial intelligence (GenAI) chatbots have shown utility in assisting with various research tasks.

Source article: Attitudes and Perceptions of Generative Artificial Intelligence Chatbots in the Scientific Process of Traditional, Complementary, and Integrative Medicine Research: A Large-Scale, International Cross-Sectional Survey

Journal of Evidence-Based Medicine
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37
Evidence-backed problemPeer-reviewedHealth

ObjectiveTo critically review the current pharmacologic treatment landscape for epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC), focusing on clinical pharmacology, therapeutic evolution, and emerging challenges of EGFR-targeted therapies.Data SourcesPeer-reviewed literature in PubMed, Web of Science, and Embase databases (January 2000 to June 2026) was searched using keywords including "EGFR-mutant NSCLC," "EGFR-TKIs," "drug resistance," "artificial intelligence," and "precision oncology." Reference lists and clinical trial registries were also screened.Data SummaryThis review systematically examines clinical misconceptions surrounding EGFR-targeted therapies, focusing on three cognitive biases that undermine precision implementation: overgeneralization of trial efficacy to unselected populations; oversimplified sequencing that ignores clonal selection trade-offs (second-generation TKIs reduce subsequent osimertinib Progression-free survival (PFS) by ∼2 months); and inflated efficacy perceptions for Ex20ins inhibitors (aggregate objective response rate (ORR) masks far-loop responses of only 22%) and MET combinations (true benefit confined to high-expression subgroup).

Source article: AI-Driven precision targeted therapy for EGFR-mutant non-small cell lung cancer: From therapeutic evolution and resistance mechanisms to intelligent decision systems

Journal of Oncology Pharmacy Practice
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38
Evidence-backed problemPeer-reviewedLifestyle

Finally, we demonstrate how Microbial Community-scale Metabolic Modeling (MCMM) and multimodal machine learning mechanistically couple specific dietary inputs with unique microbiome responses, yielding up to 95% diagnostic accuracy in differentiating diet-responsive metabolic states and individualized postprandial glycemic outcomes.

Source article: Artificial intelligence in food and nutrition science: a paradigm-centric review of computational frameworks and system-level integration

Food & Function
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39
Evidence-backed problemPeer-reviewedHealth

From Answers to Agents: What Must Change Before Generative and Agentic AI Become Clinical Infrastructure in Hematology: In 2026, a hematology agent achieved roughly 83% concordance with tumor-board decisions in a prospective silent trial, with hallucinations in 0.3%, suggesting that for well-structured tasks the binding constraint is shifting from accuracy to governability.

Source article: From Answers to Agents: What Must Change Before Generative and Agentic AI Become Clinical Infrastructure in Hematology

Turkish Journal of Hematology
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40
Evidence-backed problemPeer-reviewedCrime

Radial wall strain for residual risk stratification after percutaneous coronary intervention: Ongoing and planned prospective, randomised trials are evaluating the role of RWS-guided risk stratification.

Source article: Radial wall strain for residual risk stratification after percutaneous coronary intervention

EuroIntervention
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41
Evidence-backed problemPeer-reviewedHealth

Image-Based Diagnosis of Oral Lesions: Performance of a Vision-Language Model versus Human Clinicians: Urgency assignment was correct in 70% of cases (κ = 0.716), with a conservative tendency to overestimate risk.

Source article: Image-Based Diagnosis of Oral Lesions: Performance of a Vision-Language Model versus Human Clinicians

Otolaryngology–Head and Neck Surgery
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42
Evidence-backed problemPeer-reviewedHealth

Artificial intelligence (AI) is increasingly present in dental education and clinical practice through systems that support image interpretation, lesion detection, risk estimation, treatment planning, documentation, and access to clinical information.

Source article: Teaching AI-Informed Diagnostic Reasoning in Dental Education: A Conceptual Framework

Journal of Dental Education
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43
Evidence-backed problemPeer-reviewedHealth

An application of random forest regression for predicting healthcare costs using administrative databases: Performance was assessed at the individual-level using error metrics, and at the population-level using prediction error (PE).

Source article: An application of random forest regression for predicting healthcare costs using administrative databases

Expert Review of Pharmacoeconomics & Outcomes Research
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44
Evidence-backed problemPeer-reviewedScience

AI was primarily applied for health outcome prediction and risk stratification, whereas comparatively limited attention was directed toward preventive applications such as exposure monitoring and noise control.

Source article: Applications of artificial intelligence in occupational noise assessment and noise‑related health outcomes: a scoping review

International Journal of Environmental Health Research
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46
Evidence-backed problemPeer-reviewedHealth

[Tailoring care to the mind: The emerging role of artificial intelligence in precision psychiatry. Part II: Validating, regulating and integrating artificial intelligence into care]: Particular attention was given to discrimination, calibration, decision usefulness, external validation, bias, acceptability, interpretability, responsibility, and human oversight.

Source article: [Tailoring care to the mind: The emerging role of artificial intelligence in precision psychiatry. Part II: Validating, regulating and integrating artificial intelligence into care]

L'Encéphale
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47
Evidence-backed problemPeer-reviewedHealth

Multiscale model calibrated with a Bayesian algorithm predicts long-term outcomes of treatments for mitral valve regurgitation in animal models: In primary mitral valve regurgitation (MR), a failing valve induces volume overload (VO) of the left ventricle (LV), triggering neurohormonal responses and myocardial remodeling.

Source article: Multiscale model calibrated with a Bayesian algorithm predicts long-term outcomes of treatments for mitral valve regurgitation in animal models

Biomechanics and Modeling in Mechanobiology
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48
Evidence-backed problemPeer-reviewedEducation

To develop a machine learning (ML)-based predictive model for assessing the risk of zero-dose children using Demographic Health Survey data in sub-Saharan Africa.

Source article: Predicting zero-dose vaccination status in 27 sub-Saharan African countries: a machine learning approach

BMJ Health & Care Informatics
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49
Evidence-backed problemPeer-reviewedHealth

The objective of this study is to develop and validate a Picture Archiving and Communication System-integrated artificial intelligence (PACS-AI) tool for automated neck-level localization, three-dimensional (3D) segmentation, and metastasis risk prediction of cervical lymph nodes (LNs) in patients with papillary thyroid carcinoma (PTC).

Source article: Development and Validation of a Picture Archiving and Communication System-Integrated Artificial Intelligence for Predicting Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma

Journal of Imaging Informatics in Medicine
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50
Evidence-backed problemPeer-reviewedHealth

Artificial Intelligence in Cerebral Small Vessel Disease Imaging: A Study-Level Cross-Sectional Analysis of Validation Status and Clinical Applicability: White matter hyperintensity segmentation/quantification was the most common task (196/463, 42.3%), followed by cerebral microbleed detection/classification (82/463, 17.7%), perivascular space/lacune assessment (67/463, 14.5%), CSVD burden/risk modeling (62/463, 13.4%), and clinical outcome prediction (56/463, 12.1%).

Source article: Artificial Intelligence in Cerebral Small Vessel Disease Imaging: A Study-Level Cross-Sectional Analysis of Validation Status and Clinical Applicability

Journal of Imaging Informatics in Medicine
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52
Evidence-backed problemPeer-reviewedHealth

Artificial intelligence in ophthalmology: From diagnostic accuracy to clinical application: The primary concern is the disparity between pixel-level performance metrics and their clinical significance.

Source article: Artificial intelligence in ophthalmology: From diagnostic accuracy to clinical application

World Journal of Methodology
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53
Evidence-backed problemPeer-reviewedHealth

Artificial Intelligence in Extended Reality and Digital Rehabilitation Technologies for Fall Prevention in Older Adults: A Rapid Review of the Evidence: BackgroundFall-related injuries in older adults remain a major health concern, contributing to functional dependence and healthcare losses.

Source article: Artificial Intelligence in Extended Reality and Digital Rehabilitation Technologies for Fall Prevention in Older Adults: A Rapid Review of the Evidence

NeuroRehabilitation
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54
Evidence-backed problemPeer-reviewedClimate

Gene-environment interactions shape cytokine-mediated inflammation and cardiovascular risk: An exposome-wide association study identified environmental determinants of inflammation, and gene-environment interactions were assessed using multi-ancestry polygenic risk scores.

Source article: Gene-environment interactions shape cytokine-mediated inflammation and cardiovascular risk

European Journal of Preventive Cardiology
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55
Evidence-backed problemPeer-reviewedHealth

Can't see the forest for the trees? Statistical considerations for disease macroecology: Disease macroecology relies on large, complex datasets to understand the biotic and abiotic factors shaping parasite distributions and emerging infectious disease risk.

Source article: Can't see the forest for the trees? Statistical considerations for disease macroecology

Journal of Helminthology
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56
Evidence-backed problemPeer-reviewedHealth

Overall abstraction accuracy was 99.33% (61 errors) for the LLM versus 98.19% (164 errors) for human abstraction (McNemar p Conclusions In this proof-of-concept validation study, a customized LLM achieved significantly higher abstraction accuracy than conventional human review for general and breast reconstruction NSQIP variables.

Source article: Large Language Model Data Abstraction Demonstrates Accuracy and Reliability for NSQIP

Journal of the American College of Surgeons
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57
Evidence-backed problemPeer-reviewedHealth

This study aimed to develop and externally validate a machine learning-based prognostic model for predicting acute coronary syndrome risk at multiple time points in patients with heart failure with preserved ejection fraction and obstructive sleep apnea.

Source article: Multivariate Machine Learning Model for Long-Term Risk Prediction of Acute Coronary Syndrome in Patients With Heart Failure With Preserved Ejection Fraction and Obstructive Sleep Apnea

Journal of the American Heart Association
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58
Evidence-backed problemPeer-reviewedHealth

This narrative review maps this evolving convergence of AI and endohepatology across four functional pillars: intelligent hemodynamic assessment, virtual histology, precision tissue acquisition, and integrated risk stratification with therapeutic and decision support.

Source article: Artificial Intelligence in Endohepatology: Toward an Intelligent One-Stop Shop for Liver-Directed Endoscopy

Journal of Gastroenterology and Hepatology
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59
Evidence-backed problemPeer-reviewedHealth

Despite high accuracy, AI fundus diagnostics saw limited clinical adoption due to poor generalizability and disjointed workflow integration.

Source article: Why aren't we using AI in eye clinics? A systematic review of barriers and solutions in AI-based fundus image diagnostics for ocular diseases

International Journal of Ophthalmology
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60
Evidence-backed problemPeer-reviewedHealth

Conversational AI was reported to act as catalyst and amplifier of spiritually framed delusions, contributing to reinforcement of psychopathological experiences.

Source article: Artificial Intelligence at the Boundary of Spiritual Experience and Psychopathology: A Scoping Review of Evidence from Türkiye, the USA, the UK, Poland, South Korea, and Israel

Journal of Religion and Health
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