Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Rams, hounds and white boxes: Investigating human–AI collaboration protocols in medical diagnosis

7 Feb 2023

Abstract excerpt

In this paper, we study human-AI collaboration protocols, a design-oriented construct aimed at establishing and evaluating how humans and AI can collaborate in cognitive tasks. We applied this construct in two user studies involving 12 specialist radiologists (the knee MRI study) and 44 ECG readers of varying expertise (the ECG study), who evaluated 240 and 20 cases, respectively, in different collaboration...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Artificial Intelligence
Share to Delivery of Health Care
Share to Forecasting

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Genetic Predisposition to Disease

Share to Precision Medicine

Share to Radiomics and Machine Learning in Medical Imaging

Share to Risk Assessment

Share to Translational Research, Biomedical

Source

Identifiers

PMID: 36990586
DOI: 10.1016/j.artmed.2023.102506

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 10 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Rams, hounds and white boxes: Investigating human–AI collaboration protocols in medical diagnosisPMID 36990586Read ↗

Who should do as AI say? Only non-task expert physicians benefit from correct explainable AI adviceSemantic proximity Read ↗

A Survey of Human-AI Collaboration for Scientific DiscoverySemantic proximity Read ↗

A Survey of Human-AI Collaboration for Scientific DiscoverySemantic proximity Read ↗

Dilemma between Bias Interaction and Trustworthy AI in Human–AI Collaborated JudgmentsSemantic proximity Read ↗

Evaluating the Human Safety Net: Observational study of Physician Responses to Unsafe AI Recommendations in high-fidelity SimulationSemantic proximity Read ↗

Medical Clinical Minds Meet Artificial Intelligence: Italian Physicians’ Knowledge, Attitudes, and Concordance between Italian Physicians and AI-Generated Diagnoses. A National Cross-Sectional StudySemantic proximity Read ↗

From Human Oversight to Human-in-the-Loop: Evolving Governance of Human–AI Interaction in Healthcare and AI DevelopmentSemantic proximity Read ↗

The clinician-AI interface: intended use and explainability in FDA-cleared AI devices for medical image interpretationSemantic proximity Read ↗

From Loop to Partnership: A Framework for Understanding the Evolving Paradigms of Human-AI CollaborationSemantic proximity Read ↗

Enterprise AI Assurance Framework for Biomedical Diagnostics: A Human-Centered Approach for Trustworthy, Explainable, and Safe Artificial Intelligence SystemsSemantic proximity Read ↗

A Clinician-Led Governance Framework for Evaluating Behavioral-Health AI Communication SafetySemantic proximity Read ↗

The Cognitive Safety Net: Comparing Human and AI Diagnostic Reasoning during Complex Clinical SituationsSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.