Editing Ai Lit Review (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Scientific Literature AI Capabilities
! Task !! Current AI Capability !! Human Needed? !! Key Risk
|-
| Keyword + semantic search || Very high || Rarely || Missing niche papers
|-
| Abstract summarization (TLDR) || High || For critical decisions || Oversimplification
|-
| Full paper summarization || Moderate || For key claims || Hallucination of nuance
|-
| Inclusion/exclusion screening || High (>90% agreement) || Edge cases || Critical exclusion errors
|-
| Data extraction || Moderate-high || Verification || Numeric extraction errors
|-
| Claim synthesis/meta-analysis || Moderate || Always || Contradictions, heterogeneity
|-
| Novel hypothesis generation || Low-moderate || Always || Plausible-sounding but invalid
|}

'''Failure modes''': Hallucination — LLMs synthesizing literature can generate plausible-sounding but unsupported conclusions. Citation fabrication — models can invent non-existent papers. Publication bias — AI trained on published literature inherits the systematic bias toward positive results in published science. Cross-domain errors — models applying findings from one context to another where they don't generalize.
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