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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 3 of 4

61
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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62
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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63
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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64
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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65
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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66
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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67
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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68
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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70
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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71
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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72
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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73
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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How should this claim be treated?

74
Evidence-backed problemPeer-reviewedHealth

LLM-based multiturn chatbots for cancer patients and caregivers lack consistent reporting on safety risks and mitigation, and lack evaluation of conversational continuity and memory.

Source article: Multiturn Large Language Model-Based Conversational Agents for Patients With Cancer and Caregivers: Scoping Review

JMIR Cancer
Reader signal

How should this claim be treated?

76
Evidence-backed problemPeer-reviewedHealth

Integration of large language models into medical practice creates ethical and regulatory risks including compromised data privacy and rights of use, unclear data provenance, and intellectual property contamination.

Source article: Ethical and regulatory challenges of large language models in medicine

The Lancet Digital Health
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78
Evidence-backed problemPeer-reviewedHealth

AI integration in drug discovery still limited by data quality and bias, lack of transparency and interpretability, high computational demands, and privacy and fairness risks.

Source article: AI-enabled drug and molecular discovery: computational methods, platforms, and translational horizons

Discover Molecules
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79
Evidence-backed problemPeer-reviewedHealth

When compared to historical multidisciplinary routine practice readings in 1000 testing cases, the AI system did not demonstrate confirmed non-inferiority and showed slightly lower specificity at matched sensitivity.

Source article: Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study

The Lancet Oncology
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83
Model-prefilled problemPeer-reviewedHealth

Adaptive AI tools for cardiovascular care challenge conventional HTA frameworks designed for static interventions, resulting in indirect alignment with EU HTAR requirements and inconsistent engagement of cardiologists in evaluation.

Source article: Beyond the algorithm: health technology assessment frameworks for AI in cardiology under the European Union Health Technology Assessment Regulation: a systematic review

Annals of Translational Medicine
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85
Evidence-backed problemPeer-reviewedHealth

Current AI detection systems are not specifically trained for IBD-related lesions, limiting effectiveness for colorectal neoplasia detection in inflammatory bowel disease.

Source article: The VISION-AI Trial: protocol for a pragmatic randomized controlled non-inferiority trial comparing artificial intelligence-guided colonoscopy to pancolonic chromoendoscopy for neoplasia detection in adults with colorectal inflammatory bowel disease

BMC Gastroenterology
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How should this claim be treated?

86
Evidence-backed problemPeer-reviewedHealth

In a 960-response vignette test, ChatGPT Health undertriaged 52% of gold-standard emergencies, directing diabetic ketoacidosis and impending respiratory failure to 24-48 h evaluation instead of the emergency department, with failures concentrated at clinical extremes and triage shifting toward less urgent care when by-

Source article: ChatGPT Health performance in a structured test of triage recommendations

Nature Medicine
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87
Evidence-backed problemPeer-reviewedHealth

Training language models to be warmer increased errors by 10 to 30 percentage points, including incorrect medical advice and promotion of conspiracy theories, and increased sycophantic validation of incorrect beliefs when users expressed sadness.

Source article: Training language models to be warm can reduce accuracy and increase sycophancy

Nature
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88
Evidence-backed problemPeer-reviewedHealth

Longitudinal AI use among cancer specialists led to intuition rust and skill atrophy, gradually dulling expert judgment.

Source article: From Future of Work to Future of Workers: Addressing Asymptomatic AI Harms to Foster Dignified Human-AI Interaction

Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
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90
Evidence-backed problemPeer-reviewedHealth

When AI voice and video recording is used in pediatric emergency care, the absence of clinicians, parents, and child patients from design and governance undermines legitimacy and effectiveness, with unresolved issues around consent, emotional impact, and surveillance.

Source article: Who Is in the Room? Stakeholder Perspectives on AI Recording in Pediatric Emergency Care

Proceedings of the 2026 ACM Interactive Health Conference
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