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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 2 of 4

31
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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32
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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33
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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34
Evidence-backed problemPeer-reviewedHealth

Pediatric medical imaging is substantially underrepresented in AI development and validation, with scarce datasets and off-label use of adult models risking bias and patient safety.

Source article: Pediatric Artificial Intelligence in Radiology: Unmet Needs and Next Steps-<i>AJR</i> Expert Panel Review

American Journal of Roentgenology
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35
Evidence-backed problemPeer-reviewedHealth

Programming medical robots and AI to appear to have emotions risks creating machine paternalism, where simulated affect influences patients despite machines lacking real emotions.

Source article: Machines that express emotions: from medical paternalism to machine paternalism?

Scientific Electronic Library Online (Scientific Electronic Library Online)
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37
Evidence-backed problemPeer-reviewedHealth

In non-small-cell lung cancer, AI tools show strong benchmark performance but have repeatedly failed to translate into patient benefit because most models are retrospective, single-center, and validated only on metrics that do not track survival, toxicity, or procedural burden.

Source article: From Correlation to Clinical Translation: The Biological-Grounding×Translational-Readiness Framework for Artificial Intelligence in Non-Small-Cell Lung Cancer

American Journal of Clinical Oncology
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38
Evidence-backed problemPeer-reviewedHealth

CT, MRI, and FDG-PET/CT-based AI/radiomics models for oropharyngeal squamous cell carcinoma lack external validation, calibration, transparency, and nodal coverage, precluding safe clinical use to guide HPV-related treatment deintensification.

Source article: The Translational Gap in AI for Oropharyngeal Squamous Cell Carcinoma: A TRIPOD+AI Scoping Review of Methodological Barriers to Treatment Deintensification

Academic Radiology
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39
Evidence-backed problemPeer-reviewedHealth

The Challenges of Predicting Rare Outcomes: A Critical Appraisal of Machine Learning Using the Pediatric Resuscitation and Trauma Outcome (PRESTO) Model in a Tanzanian Injury Registry: These findings highlight the challenge of predicting a rare outcome, and emphasize the need to increase pediatric registry sample sizes to develop more accurate models for mortality risk stratification in LMICs.

Source article: The Challenges of Predicting Rare Outcomes: A Critical Appraisal of Machine Learning Using the Pediatric Resuscitation and Trauma Outcome (PRESTO) Model in a Tanzanian Injury Registry

World Journal of Surgery
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40
Evidence-backed problemPeer-reviewedHealth

When they are addressed using artificial intelligence methods, though, average performance alone does not show whether a dataset is artificial intelligence ready, whether the endpoint is clinically valid, or whether errors are unevenly distributed across patient subgroups.

Source article: From Biomedical Datasets to Fairness-Aware Recommendations: An Integrated Data Orchestration Pipeline for Binary Clinical Predictions

Computational and Structural Biotechnology Journal
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41
Evidence-backed problemPeer-reviewedHealth

Artificial intelligence-driven decision-making after endoscopic resection for early gastric cancer: Current guideline-based strategies, including the eCura system, provide structured risk stratification but rely on categorical decision-making and may lead to overtreatment, as nearly 90% of patients undergoing additional surgery do not have LNM.

Source article: Artificial intelligence-driven decision-making after endoscopic resection for early gastric cancer

Clinical Endoscopy
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43
Evidence-backed problemPeer-reviewedHealth

A novel computational solution, termed BioPINN-LM, integrates 2 computational methods for the rapid real-time prediction of aortic wall stress: a physics-informed neural network (PINN) trained with mechanical simulation data to predict wall stress and a multimodal large language model that uses output data from the PINN along with image-based geometry descriptors to provide interpretable risk assessments from both ends of the aorta.

Source article: Physics-Informed Neural Networks Meet Multimodal Large Language Models: Biomechanical Simulation in Aortic Aneurysm

Cyborg and Bionic Systems
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44
Evidence-backed problemPeer-reviewedHealth

Four machine learning (ML) algorithms (Random Survival Forest, XGBoost, Elastic Net-regularized Cox, Support Vector Machine) were trained (80%) and tested (20%) to predict overall survival (OS).

Source article: Machine learning-integrated explainable artificial intelligence for survival prediction in urothelial carcinoma with enfortumab vedotin: an exploratory real-world analysis

Expert Opinion on Biological Therapy
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45
Evidence-backed problemPeer-reviewedHealth

Objective To evaluate the accuracy, text quality, and perceived potential harm of EHR-integrated and LLM-generated hospital courses in pediatric inpatient care during early clinical implementation.

Source article: A Real-World Evaluation of Large Language Model-Generated Hospital Courses in Pediatrics

Hospital Pediatrics
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46
Evidence-backed problemPeer-reviewedHealth

Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation: Furthermore, advancements in deep learning methodologies are required to improve the accuracy of prediction concerning target functionalities and ligand characteristics, even when complete receptor structures are not available.

Source article: Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation

Molecular Biomedicine
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48
Evidence-backed problemPeer-reviewedHealth

Multimodal artificial intelligence, albeit showing great potential in computational pathology, remains limited to isolated patch-level interpretation and often fails to analyze gigapixel-scale whole-slide images (WSIs) essential for clinical utility.

Source article: SlideChat is a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types

Nature Cancer
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50
Evidence-backed problemPeer-reviewedHealth

In functional imaging, AI can facilitate automated quantification of myocardial blood flow and ischemic myocardial volume, showing excellent agreement with manual measurements and strong performance for ischemia detection and risk stratification.

Source article: Clinical Applications of Artificial Intelligence in Cardiac CT: From Coronary CT Angiography to CT-Derived Fractional Flow Reserve and Myocardial Perfusion Imaging

Korean Journal of Radiology
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51
Evidence-backed problemPeer-reviewedHealth

Post-traumatic Psychopathology and Cardiovascular Risk: A Danish Case-Control Study of Sex-Specific Associations: Background There is increasing recognition that trauma exposure and related psychiatric consequences predict cardiovascular disease risk.

Source article: Post-traumatic Psychopathology and Cardiovascular Risk: A Danish Case-Control Study of Sex-Specific Associations

Journal of the American Heart Association
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52
Evidence-backed problemPeer-reviewedHealth

A strategic pathway for the ethical development of AI tools in dementia care: Because responsible progress requires both safety and timely innovation, ethical evaluation must balance the risks of premature deployment against those of harmful delay.

Source article: A strategic pathway for the ethical development of AI tools in dementia care

Alzheimer's &amp; Dementia
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53
Evidence-backed problemPeer-reviewedHealth

In the same vignettes, experts identified safety risks in AI-generated discharge instructions not found in nurse texts, and nurses significantly outperformed AI on empathy and readability.

Source article: Evaluating the Accuracy, Empathy, and Readability of Generative AI Versus Registered Nurses in Discharge Planning: A Vignette-Based Study

Nursing Open
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54
Evidence-backed problemPeer-reviewedHealth

The same canine lymphoma cytology AI performed less favorably on B-cell versus T-cell classification, prompting concern that allowing outputs to influence reports without pathologist review and without assessing whether the specimen supports the claim could lead to unsupported clinical claims.

Source article: From accuracy to service: deciding what artificial intelligence outputs may do in veterinary diagnostic laboratories

Journal of Veterinary Diagnostic Investigation
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55
Evidence-backed problemPeer-reviewedHealth

Chi-squared selection with a neural network achieved the highest mean accuracy (74.78%; AUC 0.77).

Source article: Comparative evaluation of feature-selection strategies for machine learning-based histological grading of breast cancer using tumor and fibroglandular-tissue DCE-MRI features

Cancer Treatment and Research Communications
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56
Evidence-backed problemPeer-reviewedHealth

Large language models (LLMs) may help organize clinical information, but their use in perioperative settings requires careful evaluation because errors may have immediate safety implications.

Source article: A large language models-assisted and expert-corrected workflow for preoperative anesthesia assessment drafts: A single-centre exploratory feasibility study

Medicina Clínica
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57
Evidence-backed problemPeer-reviewedHealth

Systematic assessment of the medical utility of radiology and diagnostic Artificial Intelligence in fracture detection (SAMURAI-fracture): a protocol for a multicentre cluster-randomised controlled trial: Fracture misdiagnosis is a common diagnostic error in emergency departments (EDs) and minor injury units (MIUs), leading to poor patient outcomes, unnecessary treatments and significant healthcare costs.

Source article: Systematic assessment of the medical utility of radiology and diagnostic Artificial Intelligence in fracture detection (SAMURAI-fracture): a protocol for a multicentre cluster-randomised controlled trial

BMJ Open
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58
Evidence-backed problemPeer-reviewedHealth

These results suggest that to avoid automation bias, maintain accuracy, and achieve efficiency gains, AI deployment requires careful local adaptation.

Source article: Artificial Intelligence-Assisted Chest Radiography: A Prospective Crossover Multi-Reader Study on Diagnostic Performance and Workflow Efficiency

Academic Radiology
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59
Evidence-backed problemPeer-reviewedHealth

OpenEvidence and ChatGPT-5 demonstrated greater agreement with the expert reference standard, although clinically significant diagnostic errors were observed across all evaluated systems.

Source article: Multidisciplinary dental treatment planning by artificial intelligence: A comparative evaluation

Journal of Prosthodontics
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60
Evidence-backed problemPeer-reviewedHealth

Most artificial intelligence (AI) models used in radiology are black boxes-they produce predictions without explaining the basis of their outputs, raising concerns about clinical safety, accountability, and trust.

Source article: Explainable artificial intelligence in medical imaging: how to interpret, evaluate, and use artificial intelligence explanations

Diagnostic and Interventional Radiology
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