Evaluation of Use of Diagnostic AI for Lung Cancer in Practice

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Clinical Phase

NA

Total Enrollment

15 participants

Study Classification

INTERVENTIONAL

Study Start Date

2018-12-14

Study Completion Date

2019-12-15

Brief Summary

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This study investigates ways of improving radiologists performance of the classification of CT-scans as cancerous or non-cancerous. Participants interact with an AI to classify CT-scans under three different conditions.

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Detailed Description

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The three conditions are as follows: "probabilistic classification", where the radiologist diagnoses scans using an AI cancer likelihood score; "classification plus detection", where the radiologist see detecting lung nodules in addition to the AI's probabilistic classification score before making her own examination of the CT-scan; and "classification with delayed detection", where the radiologist identifies regions of interest independently of the AI and then sees the AI's detected ROIs.

Conditions

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Lung Cancer

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

CROSSOVER

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

SINGLE

Participants

Study Groups

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Probabilistic Classification

Radiologists see a "score" from 1-100 that represents the AI's prediction of whether the CT-scan comes from a patient with cancer or not before beginning their analysis of the scan.

Group Type EXPERIMENTAL

AI-human interaction

Intervention Type BEHAVIORAL

Exploring what kinds of AI-human interaction improve radiologists detection accuracy.

Classification Plus Detection

Radiologists see a "score" from 1-100 that represents the AI's prediction of whether the CT-scan comes from a patient with cancer or not before beginning their analysis of the scan. They also see ROIs identified by the AI that represent lung nodules.

Group Type EXPERIMENTAL

AI-human interaction

Intervention Type BEHAVIORAL

Exploring what kinds of AI-human interaction improve radiologists detection accuracy.

Classification With Delayed Detection

Radiologists see a "score" from 1-100 that represents the AI's prediction of whether the CT-scan comes from a patient with cancer or not before beginning their analysis of the scan. After identifying their own ROIs, the radiologist then can see ROIs identified by the AI that represent lung nodules before making final decisions.

Group Type EXPERIMENTAL