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Reply to: "Critical Insights on the Clinical and Technical Limitations of AI-Assisted RLN Recognition in Esophageal Surgery".
Ann Surg Oncol
December 2024
Department of Surgery, Keio University School of Medicine, Tokyo, Japan.
Refreshing the conversation about adaptation and perceived numerosity: A reply to Yousif, Clarke and Brannon.
Cognition
January 2025
Department of Psychology, Swarthmore College, USA. Electronic address:
Yousif et al. (2024) have raised a number of pertinent objections to the idea that number adaptation is a straightforward account of the readily-observable aftereffects that affect perceived numerosity. Their criticisms appear well-motivated, but their particular version of the old-news proposal, involving specific dots, may be insufficiently abstract given that adaptation accumulates.
View Article and Find Full Text PDFWhy might there be lexical-prelexical feedback in speech recognition?
Cognition
February 2025
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, NL, Netherlands; Max Planck Institute for Psycholinguistics, Nijmegen, NL, Netherlands.
Article Synopsis
- The authors challenge the conclusion that online activation feedback enhances word recognition, as proposed by Magnuson et al. (2023), arguing that the TRACE model may not accurately reflect human word recognition.
- They point out that the speed-up in word recognition could result from changes in other TRACE model parameters and that the model itself is suboptimal compared to Bayesian models.
- The authors claim that the evidence supporting activation feedback is not robust and that alternative explanations exist for the data cited by Magnuson et al., suggesting that other feedback mechanisms may be more relevant for aiding word recognition.
Enhancing Clinical Accuracy of Medical Chatbots with Large Language Models.
IEEE J Biomed Health Inform
September 2024
The rapid advancement of large language models (LLMs) has opened up new possibilities for transforming healthcare practices, patient interactions, and medical report generation. This paper explores the application of LLMs in developing medical chatbots and virtual assistants that prioritize clinical accuracy. We propose a novel multi-turn dialogue model, including adjusting the position of layer normalization to improve training stability and convergence, employing a contextual sliding window reply prediction task to capture fine-grained local context, and developing a local critical information distillation mechanism to extract and emphasize the most relevant information.
View Article and Find Full Text PDFDevelopment and Validation of a Natural Language Processing Algorithm for Extracting Clinical and Pathological Features of Breast Cancer From Pathology Reports.
JCO Clin Cancer Inform
August 2024
Division of Early Drug Development for Innovative Therapies, European Institute of Oncology IRCCS, Milan, Italy.
Purpose: Electronic health records (EHRs) are valuable information repositories that offer insights for enhancing clinical research on breast cancer (BC) using real-world data. The objective of this study was to develop a natural language processing (NLP) model specifically designed to extract structured data from BC pathology reports written in natural language.
Methods: During the initial phase, the algorithm's development cohort comprised 193 pathology reports from 116 patients with BC from 2012 to 2016.
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