Assessing Perceptions of ML Explanations by Medical Oncologists

N/AEnrolling By InvitationNCT06699615

Abramson Cancer Center at Penn Medicine50 enrolled

Overview

The objective of this proposal is to conduct a vignette-based survey among practicing oncology clinicians who treat non-small cell lung cancer to assess the trustworthiness of explainable predictions from a neurosymbolic AI vs. State-of-the-art post-hoc explanatory algorithms, using simulated patient data.

Study Type

INTERVENTIONAL

Allocation

Purpose

OTHER

Masking

NONE

Enrollment

Conditions

Oncology

Interventions

Once time de-identified Qualtrics surveyOTHER

Once time de-identified Qualtrics survey

Eligibility

Sex: ALL

Medical Language ↔ Plain English

Inclusion Criteria "Medical oncologist" or "Hematologist/Oncologist" designation at Penn Treats lung cancer Exclusion Criteria: Non-medical oncologists No email address or physical address listed Does not treat lung cancer

Locations (1)

University of Pennsylvania

Philadelphia, Pennsylvania, United States

Outcomes

Primary Outcomes

Determine whether neurosymbolic AI explainability methods improve the trustworthiness of explanations from a prognostic model, relative to post-hoc explainers.

Oncologist will be invited to complete a vignette-based survey

Time frame: 3 months

Data from ClinicalTrials.gov

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