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Record W7118021179 · doi:10.17605/osf.io/s65vh

Applications of Multimodal AI in Otolaryngology-Head and Neck Surgery: A Scoping Review

2024· other· W7118021179 on OpenAlexaff
AMEEN AMANIAN, Andrew Thamboo, Ying Jie Li, Flora Su, Philip Edgcumbe, Norbert Banyi

Bibliographic record

VenueOpen Science Framework · 2024
Typeother
Language
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMultimodalityField (mathematics)Applications of artificial intelligenceEmerging technologies

Abstract

fetched live from OpenAlex

This study explores the emerging role of multi-modal Artificial Intelligence (AI) within otolaryngology, a field seeing rapid advancement and uptake of AI. Our primary aim is to conduct a comprehensive review of the current literature, shedding light on both established and emerging uses of multimodal AI in otolaryngology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.600
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0030.020
Science and technology studies0.0000.007
Scholarly communication0.0010.001
Open science0.0070.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.010

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.395
Teacher spread0.361 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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