When Two Wrongs Don't Make a Right" -- Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology
Abstrak
Artificial intelligence (AI)-based decision support systems hold promise for enhancing diagnostic accuracy and efficiency in computational pathology. However, human-AI collaboration can introduce and amplify cognitive biases, such as confirmation bias caused by false confirmation when erroneous human opinions are reinforced by inaccurate AI output. This bias may worsen when time pressure, ubiquitously present in routine pathology, strains practitioners' cognitive resources. We quantified confirmation bias triggered by AI-induced false confirmation and examined the role of time constraints in a web-based experiment, where trained pathology experts (n=28) estimated tumor cell percentages. Our results suggest that AI integration may fuel confirmation bias, evidenced by a statistically significant positive linear-mixed-effects model coefficient linking AI recommendations mirroring flawed human judgment and alignment with system advice. Conversely, time pressure appeared to weaken this relationship. These findings highlight potential risks of AI use in healthcare and aim to support the safe integration of clinical decision support systems.
Topik & Kata Kunci
Penulis (27)
Emely Rosbach
Jonas Ammeling
Sebastian Krügel
Angelika Kießig
Alexis Fritz
Jonathan Ganz
Chloé Puget
Taryn Donovan
Andrea Klang
Maximilian C. Köller
Pompei Bolfa
Marco Tecilla
Daniela Denk
Matti Kiupel
Georgios Paraschou
Mun Keong Kok
Alexander F. H. Haake
Ronald R. de Krijger
Andreas F. -P. Sonnen
Tanit Kasantikul
Gerry M. Dorrestein
Rebecca C. Smedley
Nikolas Stathonikos
Matthias Uhl
Christof A. Bertram
Andreas Riener
Marc Aubreville
Format Sitasi
Akses Cepat
- Tahun Terbit
- 2024
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓