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

Critical Thinking II01:25

Critical Thinking II

Critical thinking is a cognitive process with several attributes. The attributes of critical thinking include the following:
Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
Reasoning01:30

Reasoning

Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
Patient-centered Care01:13

Patient-centered Care

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...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

You might also read

Related Articles

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

Sort by
Same author

Humanoid robots in the operating room: a framework for staged integration of embodied AI in surgery.

NPJ digital medicine·2026
Same authorSame journal

Small language models in medicine.

Nature biomedical engineering·2026
Same author

Biological aging clocks in health and disease.

Nature medicine·2026
Same author

Screening for Missed Opportunities for Diagnosis in the ED Using eTriggers and Large Language Models.

JAMA network open·2026
Same author

Evaluating the robustness and readiness of large frontier models in health AI applications.

Nature medicine·2026
Same author

BRIDGE: benchmarking large language models for understanding real-world clinical practice texts.

Nature biomedical engineering·2026

Related Experiment Video

Updated: Jun 25, 2026

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

Large reasoning models as thinking machines for medicine.

Hong-Yu Zhou1, Adam Rodman2, Peng Liu3

  • 1School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University, Beijing, China. hongyu.zhou.ai@gmail.com.

Nature Biomedical Engineering
|June 23, 2026
PubMed
Summary

Medical reasoning artificial intelligence (MRAI) moves beyond pattern recognition to emulate human analytical processes. This advanced AI aims to be a collaborative partner in patient care, enhancing clinical decision-making and accelerating medical discovery.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 25, 2026

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

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

Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems
  • Medical Reasoning

Background:

  • Conventional AI excels at pattern recognition but struggles with causal reasoning in complex clinical scenarios.
  • Large reasoning models offer potential to surpass correlation and mimic human analytical processes.
  • Existing AI tools have limitations in deep clinical expertise and nuanced understanding.

Purpose of the Study:

  • To introduce Medical Reasoning Artificial Intelligence (MRAI) as a new paradigm in clinical AI.
  • To envision MRAI systems that actively engage in patient care and decision-making.
  • To explore MRAI's potential to augment clinician capabilities and accelerate medical discovery.

Main Methods:

  • Conceptual framework development for MRAI systems.
  • Exploration of MRAI's integration with diverse clinical data and decision-support tools.
  • Anticipation of MRAI's learning from clinician feedback and patient outcomes.

Main Results:

  • MRAI is positioned to redefine clinical AI as a collaborative thinking partner.
  • MRAI systems are expected to provide a more nuanced understanding of complex medical scenarios.
  • MRAI can augment clinical decision-making by managing complex evidence.

Conclusions:

  • MRAI represents a paradigm shift, moving AI from a tool to a collaborative partner in healthcare.
  • This advancement is expected to enhance clinician efficiency, allowing more direct patient care.
  • MRAI holds the potential to deepen medical understanding and expedite scientific discovery.