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

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.2K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.2K
Decision Making01:20

Decision Making

253
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
253
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

638
The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic...
638
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

813
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
813
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

5.9K
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...
5.9K
Critical Thinking II01:25

Critical Thinking II

3.3K
Critical thinking is a cognitive process with several attributes. The attributes of critical thinking include the following:
3.3K

You might also read

Related Articles

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

Sort by
Same author

The interplay between the microbiome and immune cells in metabolic homeostasis and disease.

Cell metabolism·2026
Same author

Bile acid retention in efferocytic macrophages shapes their inflammatory status during cholangitis.

The Journal of experimental medicine·2026
Same author

Klebsiella genus as driver of human disease: from infections to non-communicable disorders.

Nature reviews. Microbiology·2026
Same author

Antigen-specific tolerance and control of autoimmunity effected by liver sinusoidal endothelial cells is unimpaired in liver fibrosis.

Frontiers in immunology·2026
Same author

Next-generation probiotics: an outlook into current applications and future developments.

Nature reviews. Microbiology·2026
Same author

Maternal antibodies regulate the establishment of murine oral and salivary mucosal immunity.

Nature communications·2026
Same journal

Spatially defined microenvironmental niches are associated with clinical outcome and tumor ecosystem diversity in head and neck cancer.

Med (New York, N.Y.)·2026
Same journal

AI-driven therapeutic antisense oligonucleotide for processing-deficient progeroid laminopathies.

Med (New York, N.Y.)·2026
Same journal

Advanced cholangiocarcinoma in 2025: Therapeutic sequencing and global implementation.

Med (New York, N.Y.)·2026
Same journal

Atlas of human brain imaging-derived phenotypes and disease risk.

Med (New York, N.Y.)·2026
Same journal

Withdrawal effects following treatment discontinuation: A blind spot in evidence-based medicine.

Med (New York, N.Y.)·2026
Same journal

Rethinking parenteral nutrition as supportive therapy for neonatal sepsis.

Med (New York, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Sep 22, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K

Machine learning in clinical decision making.

Lorenz Adlung1, Yotam Cohen1, Uria Mor1

  • 1Immunology Department, Weizmann Institute of Science, Rehovot 7610001, Israel.

Med (New York, N.Y.)
|May 19, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning offers clinical benefits in diagnosis and prevention but requires careful validation and ethical considerations. Addressing challenges ensures responsible integration of AI in medicine.

Keywords:
artificial intelligencecomputer-aided detection and diagnosispersonalized and precision medicinerecommendation systems

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

829
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Related Experiment Videos

Last Updated: Sep 22, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K
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

829
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Area of Science:

  • Clinical Informatics
  • Artificial Intelligence in Medicine
  • Medical Machine Learning

Background:

  • Machine learning (ML) is expanding into clinical practice for diverse applications, including diagnosis, patient stratification, and prevention.
  • Integration spans pre-clinical data analysis to real-time bedside decision support and early warning systems.

Purpose of the Study:

  • To review the promises and achievements of machine learning platforms in clinical medicine.
  • To highlight the limitations, pitfalls, and challenges hindering broader ML integration.

Main Methods:

  • Literature review focusing on the integration of machine learning in clinical settings.
  • Analysis of technological, medical, and ethical considerations for ML implementation.
  • Examination of validation, benchmarking, and knowledge dissemination strategies.

Main Results:

  • ML demonstrates significant potential across various clinical applications, from diagnostics to preventative care.
  • Key challenges include the need for rigorous real-world validation, unbiased risk-benefit assessment, and avoiding over-reliance on technology.
  • Effective integration requires addressing issues like end-user training and transparent data/code sharing.

Conclusions:

  • Successful clinical integration of machine learning hinges on addressing technological, ethical, and practical challenges.
  • Careful validation, unbiased evaluation, and responsible implementation are crucial for realizing ML's full potential in healthcare.
  • Overcoming current limitations will enhance the reliable and ethical use of AI in medical decision-making.