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

You might also read

Related Articles

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

Sort by
Same author

DiffGeo-AOR: Diffusion-Optimized Medical Grading via Geometric Priors enhanced Autoregressive Ordinal Regression.

IEEE transactions on medical imaging·2026
Same author

Beyond apoptosis: LSC state dictates metabolic and anti-apoptotic vulnerabilities.

Cell stem cell·2026
Same author

A mitochondrial-based prognostic score for risk stratification in acute myeloid leukemia.

Blood research·2026
Same author

Synthesis of <i>N</i>‑Substituted Acenaphtho[1,2‑<i>b</i>]pyrroles and Dibenzo[<i>e,g</i>]indoles with Promising Antileukemic Activity from Morita-Baylis-Hillman Adducts.

ACS omega·2026
Same author

StealthMark: Harmless and Stealthy Ownership Verification for Medical Segmentation via Uncertainty-Guided Backdoors.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Dona Flor and her two husbands: Discovery of novel HDAC6/AKT2 inhibitors for myeloid cancer treatment.

Computers in biology and medicine·2025

Related Experiment Video

Updated: Jan 10, 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

1.3K

Automated Machine Learning in medical research: A systematic literature mapping study.

Giovanna A Castro1, Luiza G Barioto1, Yu H Cao1

  • 1Department of Computer Science (DComp), Federal University of São Carlos (UFSCar), Sorocaba, 18052-780, São Paulo, Brazil.

Artificial Intelligence in Medicine
|November 22, 2025
PubMed
Summary
This summary is machine-generated.

Automated Machine Learning (AutoML) enhances healthcare efficiency by automating ML pipelines. Challenges remain in model selection and data preprocessing, with Explainable AI (XAI) integration growing to address interpretability.

Keywords:
AutoMLAutomated Machine LearningExplainable artificial intelligenceMedical data analysisMedicineSystematic mapping studyXAI

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.2K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.4K

Related Experiment Videos

Last Updated: Jan 10, 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

1.3K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.2K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.4K

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Machine Learning Applications

Background:

  • Machine Learning (ML) is crucial in healthcare but demands specialized expertise and time.
  • Automated Machine Learning (AutoML) streamlines ML integration into clinical workflows by automating pipeline steps.
  • This study maps the literature on AutoML applications in medicine.

Purpose of the Study:

  • To systematically map and analyze the application of AutoML in healthcare.
  • To identify common AutoML tasks, data types, and challenges.
  • To assess the integration of Explainable AI (XAI) with AutoML in medical research.

Main Methods:

  • Systematic literature mapping across multiple academic databases.
  • Analysis of 244 studies published between 2016 and 2025.
  • Categorization of studies by application (diagnosis, prognosis), data type (tabular, image), and ML task (classification, regression).

Main Results:

  • AutoML is predominantly used for diagnosis (52.8%) and prognosis (31.9%) prediction, primarily with tabular (43.4%) and image (31.5%) data.
  • Classification tasks (81.1%) are most common. Model selection (25.3%) and data preprocessing (13.7%) are key challenges.
  • Explainable AI (XAI) adoption is increasing (30.7%), with a notable rise in 2024, indicating a trend towards addressing AutoML's "black-box" nature.

Conclusions:

  • AutoML adoption in medicine is expanding, but its lack of interpretability hinders critical applications.
  • Integrating Explainable AI (XAI) with AutoML is a growing trend to enhance trust and clinical utility.
  • Further research is needed to evaluate the clinical impact of combined AutoML and XAI approaches for decision support systems.