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Problem Space a ranking of the record

TheProblems surrounding AI

103 problems match this view. 90% rest on at least one peer-reviewed study, and 4% clear the high-evidence bar. The latest was filed Oct 4, 2026.

Every problem in this view, one square each, shaded by the strength of its evidence. High 4 Moderate 99 Emerging 0
103 results
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Sector: health

Matching records · page 1 of 4

1
Evidence-backed problemPeer-reviewedHealth

Development of computational pathology foundation models is constrained by limited data accessibility, high variability across datasets, need for domain-specific adaptation, and lack of standardized evaluation benchmarks

Source article: A survey on computational pathology foundation models: datasets, adaptation strategies, and evaluation tasks

Knowledge and Information Systems · npj Digital Medicine
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2
Evidence-backed problemPeer-reviewedHealth

AI systems in healthcare have seen limited successful deployment into clinical practice due to intrinsic machine learning limitations and implementation barriers

Source article: Key challenges for delivering clinical impact with artificial intelligence

International Journal of Information Management · Journal of Big Data
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3
Model-prefilled problemPeer-reviewedHealth

Opaque AI models embedded in clinical trial infrastructure risk amplifying existing disparities in the evidence base by shaping who is identified and analyzed.

Source article: Embedded transparency in artificial intelligence: a prerequisite for equity and representation in AI-enabled clinical trials

npj Digital Medicine · Medicine International
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4
Evidence-backed problemPeer-reviewed ×2Health

Predict+ was recalled because the AI-enabled clinical decision support tool for shoulder replacement outcomes lacked required pre-market clearance/approval.

Source article: Blue Ortho 22 Chemin Du Vieux Chene Meylan France recalls an AI-enabled medical device

npj Digital Medicine · FDA medical-device recall 209984
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5
Evidence-backed problemPeer-reviewedHealth

Generative AI tools are being used to create or manipulate sexually explicit images using children's likenesses without physical contact, enabling commercial sexual exploitation that can occur without children's awareness and contribute to ongoing psychological harm while complicating clinical recognition and response.

Source article: Generative artificial intelligence and commercial sexual exploitation of children: Implications for healthcare providers

Child Abuse & Neglect
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6
Evidence-backed problemPeer-reviewedHealth

Despite preserved discrimination, the algorithm showed calibration failure with compressed scores, low positive predictive value and low agreement, indicating distributional shift in this low- and middle-income country setting.

Source article: Diagnostic accuracy of a DenseNet-121 deep learning algorithm for chest radiograph triage in health assessment applicants: a prospective shadow-mode validation study in Nepal

BMJ Open
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7
Evidence-backed problemPeer-reviewedHealth

Counselors reported low confidence in chatbots for sensitive result disclosure and major concerns about patient comprehension and information accuracy.

Source article: Artificial Intelligence-Based Chatbots in Genetic Counseling Practice: Current Uptake, Utilization, and Perspectives

Journal of Genetic Counseling
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8
Evidence-backed problemPeer-reviewedHealth

Identification of key molecular targets in nicotine-induced spontaneous abortion through network toxicology and multi-omics integration: Introduction Spontaneous abortion (SA) remains a prevalent reproductive health challenge, with tobacco-derived nicotine emerging as a significant risk factor.

Source article: Identification of key molecular targets in nicotine-induced spontaneous abortion through network toxicology and multi-omics integration

Tobacco Induced Diseases
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9
Evidence-backed problemPeer-reviewedHealth

Rapid serum-based differentiation of ovarian tumors and assessment of post-treatment disease-free status using portable Raman spectroscopy and machine learning: Rapid, low-cost tools for ovarian tumor discrimination and post-treatment assessment remain limited because conventional serum biomarkers such as CA125 have suboptimal sensitivity and specificity.

Source article: Rapid serum-based differentiation of ovarian tumors and assessment of post-treatment disease-free status using portable Raman spectroscopy and machine learning

Analytical and Bioanalytical Chemistry
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10
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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11
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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12
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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13
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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14
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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15
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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16
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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17
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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18
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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19
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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21
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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22
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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23
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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24
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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26
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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27
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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28
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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29
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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30
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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