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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 6 of 12

151
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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152
Evidence-backed problemPeer-reviewedPolicy

Generative AI development outpaces governance, creating risks to human autonomy, operational safety from non-deterministic outputs, and intellectual property.

Source article: Revisiting the <i>Six Human-Centered Artificial Intelligence Grand Challenges</i> in the Age of Generative AI

International Journal of Human–Computer Interaction
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154
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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155
Evidence-backed problemPeer-reviewedPolicy

The same survey found overreliance on labels, making participants more susceptible to false claims with human-made images and more hesitant to believe true claims illustrated with labeled AI-generated images.

Source article: "That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based Misinformation

Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
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157
Evidence-backed problemPeer-reviewedLabor

AI-driven automation that substitutes for routine tasks reduces demand and wages for low-skilled workers, causing absolute welfare losses for workers below a critical ability threshold and widening income inequality.

Source article: Skill-biased technological change in the age of AI: a theoretical analysis of automation and inequality

Economics of Innovation and New Technology
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158
Evidence-backed problemPeer-reviewedEducation

Generative AI now mediating core parts of learning risks crossing into manipulation and deception and accelerating drift from educational goods to metrics, reshaping development of critical thinking and creativity.

Source article: Manipulation and Deception in Generative AI-Mediated Education: Preserving Epistemic Agency, Critical Thinking, and Creativity

Postdigital Science and Education
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159
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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161
Evidence-backed problemPeer-reviewedPolicy

Unauthorised deepfakes cause disinformation, demeaning non-consensual pornographic content, and displacement of creative workers, which the current UK legal patchwork does not adequately address.

Source article: Do deepfakes, digital replicas and human digital twins justify personality rights?

The Journal of World Intellectual Property
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162
Evidence-backed problemPeer-reviewedPolicy

Mixed musical works that blend human and generative AI elements fall into a copyright 'Dead Man's Land' where the U.S. Copyright Office must inspect works case-by-case, creating an inefficient and ineffective process.

Source article: Man with Machine: The Unaddressed Copyright Issues of Mixed Musical Works

The Undergraduate Law Review at UC San Diego
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163
Evidence-backed problemPeer-reviewedPolicy

Generative AI tools capable of producing wholly or partially synthetic CSAM increase risks of revictimization of known survivors, creation of synthetic material depicting children not previously abused, and facilitation of grooming, coercion, and sexual extortion.

Source article: AI-generated child sexual abuse material: what’s the harm?

AI &amp; SOCIETY
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164
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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165
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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167
Evidence-backed problemPeer-reviewedEducation

In India, creators and users face unresolved copyright risk because the 1957 Act does not define ownership of AI-generated works or the legality of training on copyrighted data.

Source article: LEGAL CHALLENGES OF AI-GENERATED CONTENT UNDER COPYRIGHT LAW: AN INDIAN PERSPECTIVE

LawFoyer International Journal of Doctrinal Legal Research
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168
Evidence-backed problemPeer-reviewedLabor

The same vision treats human professional expertise as an extractable resource whose value is judged relative to AI, with potential to transform and revalue expert careers.

Source article: Cheap Expertise: Mapping and Challenging Industry Perspectives in the Expert Data Gig Economy

Proceedings of the 5th Annual Symposium on Human-Computer Interaction for Work
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169
Evidence-backed problemPeer-reviewedCrime

AI systems exhibit a persistent black box problem that blocks trust and adoption in high-stakes domains including healthcare, finance, and criminal justice, while dark AI uses like deepfakes and AI-powered cyberattacks create governance risks.

Source article: Beyond Generative Intelligence: A Comprehensive Review of Emerging Artificial Intelligence Paradigms, Explainability Challenges, Ethical Risks, and Future Directions

International Journal of Latest Technology in Engineering Management &amp; Applied Science
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170
Evidence-backed problemPeer-reviewedPolicy

Generative deepfake systems produce reputational, identity-based and corporate harms through chains of developers, prompting users, platforms and distributors that ordinary Saudi and Jordanian tort doctrine struggles to remedy.

Source article: Deepfake-induced harm and AI accountability: a layered civil-liability framework for generative models, platforms, and digital identity

Frontiers in Artificial Intelligence
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171
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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174
Evidence-backed problemPeer-reviewedLifestyle

Among furniture artisans in South-East Nigeria, AI-facilitated superwood utilization for sustainable manufacturing was hindered by severe knowledge deficits, low substantive AI understanding, low perceived ease of use, and unreliable electricity and internet

Source article: The role of artificial intelligence in facilitating superwood utilization among furniture craft producers for sustainable smart manufacturing

Scientific Reports
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177
Active harmPeer-reviewedHealth

AI-driven differential-diagnosis tools show omission and commission error patterns that haven't been evenly studied across race, age, or income groups — meaning the tool's blind spots may track existing healthcare gaps rather than closing them.

Source article: Diagnostic bias across underrepresented patient groups

NCBI randomized controlled study on AI-driven differential diagnosis
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