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 Cognitive Psychology01:20

Introduction to Cognitive Psychology

2.2K
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...
2.2K
Reason and Intuition01:37

Reason and Intuition

7.4K
The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
7.4K
Current Trends in Nursing II01:30

Current Trends in Nursing II

3.3K
Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...
3.3K

You might also read

Related Articles

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

Sort by
Same author

Computer Says: I Don't Know? - On Epistemic Humility as a Condition for Human-AI Collaboration.

The American journal of bioethics : AJOB·2026
Same author

Digital bioethics: exploring an emerging field.

Medicine, health care, and philosophy·2026
Same author

Ethical gaps in closed-loop neurotechnology: a scoping review.

NPJ digital medicine·2025
Same author

AI, neurotechnology and society - a question of trust.

Nature reviews. Neurology·2025
Same author

On Religious Influence in Bioethics: The Limits of Pluriversalism.

Bioethics·2025
Same author

Epistemic humility meets virtual reality: teaching an old ideal with novel tools.

Journal of medical ethics·2025

Related Experiment Video

Updated: Jan 16, 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

Clinician perspectives on explainability in AI-driven closed-loop neurotechnology.

Laura Schopp1, Georg Starke1, Marcello Ienca2

  • 1Laboratory of Ethics of AI and Neuroscience, Institute of History and Ethics in Medicine, School of Medicine and Health, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, München, Germany.

Scientific Reports
|October 3, 2025
PubMed
Summary

Clinicians need clinically meaningful explanations, not technical details, for AI in neurotechnology. Focusing on user-centered explainable AI (XAI) can improve adoption and clinical translation.

Keywords:
Clinical perspectiveExplainable artificial intelligenceNeurological diseaseNeurostimulationNeurotechnologyPsychiatric disorderSemi-structured expert interviews

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.8K
Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

5.1K

Related Experiment Videos

Last Updated: Jan 16, 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
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.8K
Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

5.1K

Area of Science:

  • Neurotechnology
  • Artificial Intelligence
  • Clinical Translation

Background:

  • Artificial Intelligence (AI) shows potential for neurotechnology advancement and clinical application.
  • AI-driven neurotechnologies use complex algorithms for brain data analysis and closed-loop neurostimulation.
  • Limited clinical integration of AI is often due to a lack of explainability.

Purpose of the Study:

  • To investigate clinician attitudes towards AI-driven closed-loop neurotechnologies.
  • To explore clinicians' informational needs and preferences regarding AI explainability.
  • To determine necessary forms of explanation for clinical AI adoption.

Main Methods:

  • Conducted semi-structured expert interviews with 20 clinicians (neurologists, neurosurgeons, psychiatrists) in Germany and Switzerland.
  • Employed reflexive thematic analysis to understand clinician expectations for AI explainability.
  • Focused on AI in closed-loop neurotechnology systems.

Main Results:

  • Clinicians prioritize context-sensitive, clinically meaningful explanations (e.g., input data, outcome relevance).
  • Detailed technical model information was of low interest to clinicians.
  • Clinicians specifically requested Explainable AI (XAI) techniques like feature importance.

Conclusions:

  • Clinical utility of AI neurotechnologies can be enhanced by focusing on intuitive, user-centered, and clinically relevant explainability.
  • Prioritizing pragmatic clinician needs over full algorithmic transparency can bridge the AI development-to-clinical implementation gap.
  • Designing AI systems with clinician-focused explainability is crucial for successful adoption.