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 Experiment Videos

How to benchmark medical AI agents.

Silas Ruhrberg Estévez1,2,3, Dyke Ferber1, Mihaela van der Schaar2,3

  • 1Else Kroener Fresenius Center for Digital Health, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.

Plos Medicine
|July 9, 2026
PubMed
Summary

Related Concept Videos

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

You might also read

Related Articles

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

Sort by
Same author

AI-based selection of tumor regions for genomic profiling in neuropathology.

Neuro-oncology advances·2026
Same author

Clinical decision support in hematological malignancies using a case-grounded AI agent.

Nature medicine·2026
Same author

A deep learning framework for efficient pathology image analysis.

Nature communications·2026
Same author

Causal inference and digital twins: a roadmap for the future of clinical trials.

NPJ digital medicine·2026
Same author

Functional Outcome Prediction in Young Adults With Mental Health Symptoms Using Machine Learning and Large Language Models: Longitudinal Observational Study.

JMIR mental health·2026
Same author

Translation readiness of model-based synthetic tabular data in healthcare: a systematic review and governance audit.

Journal of the American Medical Informatics Association : JAMIA·2026
This summary is machine-generated.

Medical artificial intelligence is moving towards multimodal large language models for clinical workflows. New benchmarks are needed to evaluate reasoning, safety, and resource use, not just final outcomes.

Area of Science:

  • Artificial intelligence in medicine
  • Clinical workflow optimization
  • Large language models

Background:

  • Current medical AI research focuses on single-task models.
  • There is a growing trend towards multimodal large language model-based agents.
  • These agents are being developed for complex clinical workflows.

Purpose of the Study:

  • To highlight the shift in medical AI research.
  • To emphasize the need for new evaluation benchmarks.
  • To advocate for benchmarks assessing clinical reasoning, process safety, and resource stewardship.

Main Methods:

  • Analysis of current trends in medical AI research.
  • Identification of limitations in existing evaluation methods.
  • Proposal for a new benchmark framework.

Related Experiment Videos

Main Results:

  • Single-task models are insufficient for complex clinical workflows.
  • Multimodal large language models offer greater potential.
  • Existing benchmarks primarily focus on final outputs, neglecting critical process elements.

Conclusions:

  • Medical AI evaluation must evolve beyond simple output assessment.
  • Benchmarks should incorporate clinical reasoning, process safety, and resource stewardship.
  • This shift is crucial for the responsible integration of AI in healthcare.