Case-Based Clinical Reasoning in Practice

Zijl, Angela van; Loon, Maria van; Cate, Olle ten

doi:10.1007/978-3-319-64828-6_6

Cited by 3 publications

(1 citation statement)

References 4 publications

(6 reference statements)

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“…AI refers to the use of computers and advanced technologies to simulate human intelligent behavior and critical thinking, 12 which has achieved great success in research and application. 13 AI includes many different technologies: intelligent agent, 14 symbolic and subsymbolic reasoning, 15 plan, 16 case-based reasoning, 17 fuzzy systems, 18 expert systems, 19 and so on. The tool that enables most of these techniques is machine learning (ML).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

Yuan,

An,

Zhu

et al. 2024

CMAR

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As a disease with high morbidity and high mortality, lung cancer has seriously harmed people’s health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.

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Section: Introductionmentioning

confidence: 99%

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

Yuan,

An,

Zhu

et al. 2024

CMAR

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Case-Based Learning

Loftus

2022

An Introduction to Medical Teaching

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A clinical reasoning curriculum for medical students: an interim analysis

2021

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Objectives Diagnostic error is a critical patient safety issue that can be addressed in part through teaching clinical reasoning. Medical schools with clinical reasoning curricula tend to emphasize general reasoning concepts (e.g., differential diagnosis generation). Few published curricula go beyond teaching the steps in the diagnostic process to address how students should structure their knowledge to optimize diagnostic performance in future clinical encounters or to discuss elements outside of individual cognition that are essential to diagnosis. Methods In 2016, the University of California, San Francisco School of Medicine launched a clinical reasoning curriculum that simultaneously emphasizes reasoning concepts and intentional knowledge construction; the roles of patients, families, interprofessional colleagues; and communication in diagnosis. The curriculum features a longitudinal thread beginning in first year, with an immersive three week diagnostic reasoning (DR) course in the second year. Students evaluated the DR course. Additionally, we conducted an audit of the multiyear clinical reasoning curriculum using the Society to Improve Diagnosis in Medicine-Macy Foundation interprofessional diagnostic education competencies. Results Students rated DR highly (range 4.13–4.18/5 between 2018 and 2020) and reported high self-efficacy with applying clinical reasoning concepts and communicating reasoning to supervisors. A course audit demonstrated a disproportionate emphasis on individual (cognitive) competencies with inadequate attention to systems and team factors in diagnosis. Conclusions Our clinical reasoning curriculum led to high student self-efficacy. However, we stressed cognitive aspects of reasoning with limited instruction on teams and systems. Diagnosis education should expand beyond the cognitive- and physician-centric focus of most published reasoning courses.

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Case-Based Clinical Reasoning in Practice

Cited by 3 publications

References 4 publications

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

Case-Based Learning

A clinical reasoning curriculum for medical students: an interim analysis

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