Aug 2026· Journal of Speech, Language and Hearing Research· pp.
1-8
· 0 citations· 12 references
Medicine
TL;DR
Clinical reasoning is stronger when observation and interpretation are kept separate, categories are used for communication rather than as explanations, and the evidence that would change a decision is named in advance.
Abstract
Purpose
Speech-language pathologists often have to make consequential decisions with evidence that is incomplete, indirect, or mixed. The risk is not uncertainty itself, but the way an indirect measure, a familiar label, or a best-practice statement can begin to carry more interpretive weight than it can support. This tutorial focuses on the point where observations are turned into claims and claims are turned into recommendations.
Method
The tutorial draws on scholarship in measurement and assessment showing that a score or result only has meaning through how it is interpreted and how it is used in practice. It also draws on clinical reasoning scholarship, including dual-process models, to support a decision rationale that is stated clearly, remains open to revision, and is tied to clear conditions for changing course.
Results
Across swallowing, language assessment, and hearing health, the same patterns appear. Measures that are genuinely informative are read as more definitive than they are, and labels meant to support communication begin to function as explanations. The tutorial offers brief checkpoints that ask what the current observations support, what else they could reasonably mean, and what new information would change the decision.
Conclusions
Clinical reasoning is stronger when observation and interpretation are kept separate, categories are used for communication rather than as explanations, and the evidence that would change a decision is named in advance. This tutorial supports evidence-based practice by making the reasoning trail visible while still respecting the need to act in real time.
Current evidence supports clinician-supervised use of artificial intelligence (AI) systems rather than autonomous diagnosis, pending prospective and specialty-specific evaluation.
Yun-Jia Wu, Qi Yan, Dingcheng Tian· AI Medicine· 0 citations
A paired benchmark for measuring outcome-conditioned shifts consistent with hindsight bias in clinical temporal reasoning is introduced and accuracy (Acc), hindsight trap rate (HTR), answer instability rate (AIR), and hindsight bias rate (HBR) are evaluated.
Misaki Matsuura, Sayantan Kumar, Ojas Kadam et al.· 1 citation
Generative artificial intelligence can now produce plausible and sometimes correct responses to complex learning tasks, making answer quality a less reliable indicator of students' understanding and judgment. When artificial intelligence contributes to information seeking, synthesis, interpretation, and answer generati...
Models often produced evidence-consistent numbers but failed to adapt them to a case or act consistently with their own stated cutoff, so explicit inputs, calculations and complete rules substantially improved performance in controlled prompts.
S. He, J. Joseph, P. Safari et al.· medRxiv· 0 citations
Exam-style accuracy does not establish whether large language models (LLMs) reason well over clinical records. We define clinical reasoning as integrating and updating evidence across time and sources to form, revise and justify a patient's problem representation and a defensible plan. This structured narrative review...
Medical large language models are evaluated through factuality, physician preference, source support, retrieval quality, safety, and clinical utility. These measures describe content and performance while leaving unresolved whether a generated claim meets the requirements for a specified clinical use. This article prop...
Murat Sariyar· Frontiers in Digital Health· 0 citations
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