Correction: Towards Safe and Trustworthy Healthcare AI: Risk Assessment of Medical Dialogue Using LLMs

The Note at the bottom of Table 6 was corrected from "x : means ROC-AUC value; SE: standard error; df: degree of freedom; LCL and UCL: lower and upper 95 confidence limits; p: two-tailed significance level; Sig.: column indicates statistical significance (* = p < 0.05, ** = p < 0.01, ** = p < 0.001)" to "[x ] means ROC-AUC value; SE: standard error; df: degree of freedom; LCL and UCL: lower and upper 95 confidence limits; p: two-tailed significance level; Sig.: column indicates statistical significance (* = p < 0.05, ** = p < 0.01, *** = p < 0.001)".The Note at the bottom of Table 7 was corrected from "x : means ROC-AUC value; SE: standard error; df: degree of freedom; LCL and UCL: lower and upper 95 confidence limits; p: two-tailed significance level; Sig.: column indicates statistical significance (* = p < 0.05, ** = p < 0.01, ** = p < 0.001)" to "n: number of samples; x > 0.5, x < 0.5, x = 0.5: counts of ROC-AUC values above, below, and equal to the random baseline (0.5); p: two-tailed significance level from the sign test; Sig.column indicates statistically significant differences (* = p < 0.05,

Authors

Institutions

Publication Details

Journal
Human-Centric Intelligent Systems
Published
2026-03-27
DOI
https://doi.org/10.1007/s44230-026-00142-9
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Correction: Towards Safe and Trustworthy Healthcare AI: Risk Assessment of Medical Dialogue Using LLMs

Hiroki Tanioka
Human-Centric Intelligent Systems
Artificial Intelligence in Healthcare and Education
article

Correction: Towards Safe and Trustworthy Healthcare AI: Risk Assessment of Medical Dialogue Using LLMs

Hiroki Tanioka
article en

Abstract

The Note at the bottom of Table 6 was corrected from "x : means ROC-AUC value; SE: standard error; df: degree of freedom; LCL and UCL: lower and upper 95 confidence limits; p: two-tailed significance level; Sig.: column indicates statistical significance (* = p < 0.05, ** = p < 0.01, ** = p < 0.001)" to "[x ] means ROC-AUC value; SE: standard error; df: degree of freedom; LCL and UCL: lower and upper 95 confidence limits; p: two-tailed significance level; Sig.: column indicates statistical significance (* = p < 0.05, ** = p < 0.01, *** = p < 0.001)".The Note at the bottom of Table 7 was corrected from "x : means ROC-AUC value; SE: standard error; df: degree of freedom; LCL and UCL: lower and upper 95 confidence limits; p: two-tailed significance level; Sig.: column indicates statistical significance (* = p < 0.05, ** = p < 0.01, ** = p < 0.001)" to "n: number of samples; x > 0.5, x < 0.5, x = 0.5: counts of ROC-AUC values above, below, and equal to the random baseline (0.5); p: two-tailed significance level from the sign test; Sig.column indicates statistically significant differences (* = p < 0.05,

Human-Centric Intelligent Systems
Tokushima University (JP)
Openalex Percentile: Top 7%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.