Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Introduction to Language of Pathophysiology ll01:17

Introduction to Language of Pathophysiology ll

3
This lesson explores key terms that describe how diseases progress, their outcomes, and their distribution in populations.Diagnostic tests identify diseases and monitor treatment. These include blood and urine tests, biopsies, imaging (X-ray, MRI), and detection of infectious agents.Remission is a reduction or disappearance of symptoms.Exacerbation refers to the worsening of symptoms, such as increased wheezing during an asthma attack.A precipitating factor triggers an acute episode, while a...
3
Critical Thinking II01:25

Critical Thinking II

5.3K
Critical thinking is a cognitive process with several attributes. The attributes of critical thinking include the following:
5.3K
Introduction to Language of Pathophysiology l01:25

Introduction to Language of Pathophysiology l

10
Pathophysiology investigates how biological mechanisms—typically starting at the cellular level—disrupt normal bodily functions. It bridges anatomy and physiology to explain the progression of disease. With this foundation, it is important to understand the following key terms used to describe disease processes: Diagnosis:The process of identifying a disease using clinical evaluation, including signs (objective evidence like rashes), symptoms (subjective experiences like...
10
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

415
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
415
Mathematical Modeling: Problem Solving01:29

Mathematical Modeling: Problem Solving

544
Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
544
Patient-centered Care01:13

Patient-centered Care

3.4K
Patient-centered care involves delivering care beyond inpatient hospitalization. Reflective practice can enhance a patient-centered approach. Reflective practice is a process of reasoning that considers all aspects of the present situation, including practicalities, learning from personal practice, and consideration of patient needs. Patients appreciate care decisions made while considering their input. Involving the patient in their care provides the patient with a sense of contribution rather...
3.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Case Report: Early recognition of neonatal alpha-1 antitrypsin deficiency: a case of subtle presentation and prompt diagnosis.

Frontiers in pediatrics·2026
Same author

Dual oral iron chelation with deferasirox and deferiprone in transfusion-dependent β-thalassemia: a narrative review of efficacy, safety, and practical application.

Annals of hematology·2026
Same author

Case Report: A classical PSGN case with unusually prominent serosal manifestations and complement patterns that mimicked systemic autoimmune disease-highlighting diagnostic pitfalls and biopsy decision-making.

Frontiers in pediatrics·2026
Same author

Science of the burp: understanding aerophagia and eructation in newborns.

BMJ paediatrics open·2025
Same author

Paraneoplastic minimal change disease in malignant thymoma: a rare association progressing to end-stage renal disease.

Oxford medical case reports·2025
Same author

The Utility of Troponin in Predicting Cardiac Dysfunction in Pediatric Patients: A Meta-Analysis.

Cureus·2025

Related Experiment Video

Updated: Apr 19, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.3K

Input structure-driven instability and convergence in large language model clinical reasoning: a formative study

Vinson James1, Catherine Caronia2, Rajesh Savargaonkar2

  • 1Department of Pediatrics, Good Samaritan University Hospital, West Islip, NY, 11795, USA. dr.vinsonjames@gmail.com.

Scientific Reports
|April 17, 2026
PubMed
Summary

Large language models (LLMs) show unstable clinical reasoning when given many questions at once. Structuring questions in batches significantly improves LLM performance consistency and reliability in medical education.

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.9K
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.3K

Related Experiment Videos

Last Updated: Apr 19, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.3K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.9K
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.3K

Area of Science:

  • Artificial Intelligence in Medical Education
  • Clinical Reasoning Assessment
  • Large Language Model (LLM) Performance

Background:

  • Large language models (LLMs) are increasingly used in medical education and clinical settings.
  • Prior research focused on LLM accuracy on exams, but not on the stability of clinical reasoning with varied input structures.
  • Input structure is critical for safe educational and clinical deployment of LLMs.

Purpose of the Study:

  • To investigate how question delivery structure affects performance stability, inter-model variability, and reproducibility of LLM clinical reasoning.
  • To evaluate contemporary LLMs using pediatric residency-level multiple-choice questions (MCQs).

Main Methods:

  • Generated 77 validated pediatric USMLE Step 2/3-style MCQs emphasizing diagnostic, management, and ethical reasoning.
  • Evaluated six publicly available LLMs (October-December 2025 versions) under two conditions: simultaneous presentation vs. sequential delivery in batches of ten.
  • Compared accuracy and inter-model variability using paired t-tests and one-way ANOVA.

Main Results:

  • Simultaneous question presentation showed wide accuracy variation (38%-90%) and poor reproducibility across models.
  • Sequential batch delivery improved performance convergence (83%-88%) with no significant inter-model differences.
  • Batch delivery substantially reduced performance dispersion and instability across all evaluated LLMs.

Conclusions:

  • LLM clinical reasoning is highly sensitive to input structure; prompt structure is key to reliable behavior.
  • Structured batch delivery minimizes contextual load, improving reproducibility and reducing inter-model variability.
  • Consider prompt structure in designing AI-supported medical education and assessment systems for reliable LLM use.