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Related Concept Videos

Visual Agnosia01:12

Visual Agnosia

Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round end"...

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Increasing transparency of computer-aided detection impairs decision-making in visual search.

Melina A Kunar1, Giovanni Montana2, Derrick G Watson3

  • 1Department of Psychology, The University of Warwick, Coventry, CV4 7AL, UK. m.a.kunar@warwick.ac.uk.

Psychonomic Bulletin & Review
|October 24, 2024
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Summary

Providing details on artificial intelligence (AI) accuracy in medical screening may harm performance. Increased AI transparency led to more errors and decreased diagnostic accuracy in a simulated mammography task.

Keywords:
Artificial intelligenceComputer-aided detection (CAD)Low prevalenceOverrelianceTransparencyVisual search

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Area of Science:

  • Medical imaging
  • Artificial intelligence in healthcare
  • Human-computer interaction

Background:

  • Artificial intelligence (AI) is increasingly used in healthcare, prompting calls for transparency in AI systems.
  • Transparency recommendations aim to inform users about AI accuracy and functionality.
  • However, enhanced transparency may lead to overreliance and negative outcomes in human decision-making.

Purpose of the Study:

  • To investigate the impact of AI transparency on human decision-making in a medical screening context.
  • To assess how knowledge of AI accuracy affects performance in a visual search task.

Main Methods:

  • A simulated laboratory mammography task was used, involving visual search for cancer.
  • Participants' performance was evaluated under two conditions: transparent (AI accuracy disclosed) and non-transparent (AI accuracy withheld).
  • Computer-aided detection (CAD) systems with varying accuracies provided AI prompts.

Main Results:

  • Increased AI transparency impaired task performance.
  • The transparent condition resulted in more false alarms and decreased sensitivity.
  • Recall rate increased, and positive predictive value decreased with greater transparency.

Conclusions:

  • Transparency in AI systems, particularly in medical screening, can negatively affect human decision-making.
  • Overtrust in AI due to increased transparency may lead to adverse clinical outcomes.
  • Further research is crucial to understand the complex relationship between AI transparency and human performance in healthcare settings.