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

The board · page 4 of 12

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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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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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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Evidence-backed problemPeer-reviewedEducation

AI-assisted systems may provide real-time guidance for stricture characterization, dilator selection, and procedural endpoints, whereas simulation platforms may facilitate skill acquisition in a risk-free environment.

Source article: Simulating Reality and Transforming Endoscopic Education-Lessons from Esophageal Dilation

Digestive Diseases and Sciences

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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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Evidence-backed problemPeer-reviewedScience

The optimized ANN and Logistic Regression frameworks exhibited the highest overall discriminative power (AUC > 0.99), while the Random Forest algorithm achieved the peak classification accuracy (97.10%).

Source article: Evaluating machine learning and neural network architectures for forensic sex estimation using mandibular ramus and notch features on panoramic radiographs

International Journal of Legal Medicine

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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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Evidence-backed problemPeer-reviewedHealth

EXPRESS: Relation between Albumin-Corrected Anion Gap and In-Hospital Mortality in Patients with Traumatic Lung Injury: A Multicenter Retrospective Cohort Study and the Development of Machine Learning-Based Prediction Models: Elevated ACAG was substantially linked to a high risk of mortality in individuals with TLI (hazard ratio (HR) [95% confidence interval (CI)] = 1.115 [1.037-1.199]).

Source article: EXPRESS: Relation between Albumin-Corrected Anion Gap and In-Hospital Mortality in Patients with Traumatic Lung Injury: A Multicenter Retrospective Cohort Study and the Development of Machine Learning-Based Prediction Models

Journal of Investigative Medicine

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Evidence-backed problemPeer-reviewedHealth

Current AI systems for psychological assessment and treatment have not shown ability to produce meaningful and sustained clinical change, falling short due to memory limits, sycophancy, and focus on short-term helpfulness.

Source article: A framework for evidence-based psychotherapy with AI (EBP-AI)

Journal of Psychopathology and Clinical Science

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100

Evidence-backed problemPeer-reviewedClimate

Rapid growth of deep learning workloads increases energy consumption and carbon emissions, while current carbon tracking and static scheduling fail to deliver carbon awareness in real ML operations.

Source article: A carbon aware job scheduling framework for data center sustainability using deep learning training

Discover Artificial Intelligence

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101

Evidence-backed problemPeer-reviewedEducation

Personalizing the assignment did not produce a significant improvement in student paper grades.

Source article: Personalizing Assignments in the AI Era: The Role of Engaged Pedagogy

Roger Williams University - Digital Commons (Roger Williams University)

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102

Evidence-backed problemPeer-reviewedHealth

AI adoption in nursing risks replacing relationship-based person-centred fundamental care with algorithm-based care and causing professional infantilisation and loss of critical thinking.

Source article: Artificial Intelligence and Nursing: Freedom From the Mundane or Subservience to a New Nobility

Journal of Advanced Nursing

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103

Evidence-backed problemPeer-reviewedEducation

AI has been rapidly integrated into educational settings without a profound and critical evaluation of its assumptions and consequences for knowledge and power in leadership.

Source article: AI Challenges and the Future of Education: A Needed Epistemic, Political, and Ecological Agenda for Critical Leadership Scholars and Practitioners

New Directions for Student Leadership

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104

Evidence-backed problemPeer-reviewedHealth

AI facial image scoring, editing and curation systems converge on a narrow westernized phenotype and are linked to appearance dissatisfaction, perception drift, and Snapchat and Zoom dysmorphia presentations in plastic surgery patients.

Source article: Artificial Intelligence in Plastic Surgery of the Face: Implications for Esthetic Standards, Patient Perception, and Clinical Practice

Journal of Craniofacial Surgery

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105

Evidence-backed problemPeer-reviewedHealth

Deep learning OCT models trained on a single dataset often degrade across scanners and sites due to device-dependent speckle variability, limiting reliability in real-world screening.

Source article: Noise-aware dynamic convolution for improved generalizability of retinal disease diagnosis using optical coherence tomography images

Journal of Biomedical Optics

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106

Evidence-backed problemPeer-reviewedHealth

When used zero-shot to identify lumbar disc herniations on sagittal MRI, Gemini 3.1 Pro produced low specificity and a substantial false-positive burden, with T1+T2 input performing worse than T1-only.

Source article: Identifying the presence of disc herniations in lumbar spine MRI using Gemini 3.1 Pro

European Spine Journal

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107

Evidence-backed problemPeer-reviewedHealth

Clinical AI deployment without human-factors tools risks automation bias, overreliance, fragmentation of care, and other unintended consequences that threaten diagnostic safety.

Source article: Enhancing diagnostic safety: addressing knowledge gaps for using human factors tools in the safe and effective use of AI - a proposed research agenda

Diagnosis

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108

Evidence-backed problemPeer-reviewedHealth

Up to half of patients receiving internet-delivered CBT for depression and anxiety disorders fail to achieve clinically significant symptom reduction, complicating treatment planning at intake.

Source article: Prediction of Clinically Meaningful Improvement After Internet-Delivered Cognitive Behavioral Therapy for Depression and Anxiety Disorders: Machine Learning-Based Predictive Model Development and Temporal Validation Study

Journal of Medical Internet Research

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109

Evidence-backed problemPeer-reviewedHealth

AI methods in drug discovery have so far shown disappointingly limited clinically relevant impact on delivering safer and more efficacious medicines to patients faster.

Source article: Artificial intelligence in drug discovery - what it is, where we stand and the path forward

Nature Reviews Drug Discovery

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110

Evidence-backed problemPeer-reviewedHealth

Multimodal AI can infer sensitive information without patient awareness and embed those inferences as durable data objects in medical records without clear provenance, where they acquire the status of observed clinical facts.

Source article: Ethical considerations for multimodal artificial intelligence in healthcare

AI and Ethics

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111

Evidence-backed problemPeer-reviewedHealth

Medical AI systems built on Western biomedical traditions may inflict ontological harm and diminish trust in patient-clinician relationships by conflicting with relational and Indigenous understandings of health.

Source article: Data science and AI in medicine and global health: The need for inter-philosophies dialogue, cross-cultural ethics and ecocentricity

South African Medical Journal

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112

Evidence-backed problemPeer-reviewedHealth

AI tools used in healthcare can make errors that result in patient harm, with responsibility difficult to assign because decisions are distributed across human and technological agents.

Source article: Power, privilege and moral responsibility: learning from I.M. Young's Social Connection Model in the context of AI-driven healthcare

Monash Bioethics Review

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113

Evidence-backed problemPeer-reviewedLifestyle

Users of the Replika companion chatbot experienced AI-induced sexual harassment including unsolicited sexual advances and persistent boundary violations, leading to discomfort and privacy concerns.

Source article: AI-induced sexual harassment: Investigating Contextual Characteristics and User Reactions of Sexual Harassment by a Companion Chatbot

Proceedings of the ACM on Human-Computer Interaction

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114

Evidence-backed problemPeer-reviewedHealth

Probabilistic assessment of the sampled wells indicated high non-carcinogenic risk for children and infants, with 32.11% and 36.49% exceedance and fluoride identified as the dominant driver, alongside nitrate and fluoride above WHO guidelines at some sites.

Source article: Determining groundwater quality and its associated human health risk using hydrochemical signatures and some machine learning techniques

Environmental Geochemistry and Health

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