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How do dementia patients and their relatives perceive AI assistance in clinical decision making?

Poster No:

584 

Submission Type:

Abstract Submission 

Authors:

Martin Dyrba1, Laura Schreiter1, Olga Biernetzky1, Devesh Singh1, Stefanie Köhler1, Doreen Goerss1,2, Ingo Kilimann1,2, Eike Buhr3, Mark Schweda3, Stefan Teipel1,2

Institutions:

1German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany, 2Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany, 3Dept. for Health Services Research, Division of Ethics in Medicine, Carl von Ossietzky Universität, Oldenburg, Germany

First Author:

Martin Dyrba  
German Center for Neurodegenerative Diseases (DZNE)
Rostock, Germany

Co-Author(s):

Laura Schreiter  
German Center for Neurodegenerative Diseases (DZNE)
Rostock, Germany
Olga Biernetzky  
German Center for Neurodegenerative Diseases (DZNE)
Rostock, Germany
Devesh Singh  
German Center for Neurodegenerative Diseases (DZNE)
Rostock, Germany
Stefanie Köhler  
German Center for Neurodegenerative Diseases (DZNE)
Rostock, Germany
Doreen Goerss  
German Center for Neurodegenerative Diseases (DZNE)|Department of Psychosomatic Medicine, Rostock University Medical Center
Rostock, Germany|Rostock, Germany
Ingo Kilimann  
German Center for Neurodegenerative Diseases (DZNE)|Department of Psychosomatic Medicine, Rostock University Medical Center
Rostock, Germany|Rostock, Germany
Eike Buhr  
Dept. for Health Services Research, Division of Ethics in Medicine, Carl von Ossietzky Universität
Oldenburg, Germany
Mark Schweda  
Dept. for Health Services Research, Division of Ethics in Medicine, Carl von Ossietzky Universität
Oldenburg, Germany
Stefan Teipel  
German Center for Neurodegenerative Diseases (DZNE)|Department of Psychosomatic Medicine, Rostock University Medical Center
Rostock, Germany|Rostock, Germany

Introduction:

As the number of older people is rapidly increasing, we are facing a higher demand of diagnostic services, for example to detect neurodegenerative diseases. At the same time, the number of medical centers and experts remains almost constant, which poses a challenge. Tools for diagnostic assistance are urgently needed to improve the efficiency of healthcare. In the project "Theoretical, Ethical and Social Implications of Computational Psychiatry" (TESIComP), funded by the German Federal Ministry of Education and Research (BMBF), we are investigating the ethical and social aspects of diagnostic AI tools. In particular, we focus on two use cases of AI-based evaluation of neuroimaging data for patterns of dementia and depression.

Methods:

In the project "ExplAInation" funded by the German research foundation (DFG), we have developed a deep learning framework that generates visual and textual explanations (Fig. 1) to improve the comprehensibility and interpretability of deep learning models for AI-based evaluation of MRI scans (Dyrba et al., 2021). A prototype app supporting the evaluation of brain MRI scans for the risk of Alzheimer's dementia is currently being evaluated. For the dementia use case in TESIComP, we first interviewed 10 memory clinic physicians and ethics experts. Second, we conducted three focus group discussions with doctors, people with dementia and caregivers to identify the opportunities and challenges of AI-based clinical decision support. Third, we performed a participant observation in which the prototype app was shown by the doctor to (N=15) patients and their relatives during the consultation to examine how these AI tools might change the patient-doctor relationship.
Supporting Image: OHBM-2025-Abstract-Dyrba-TESIComP-Fig1.png
   ·Fig.1 Interactive deep learning app for the evaluation of MRI scans
 

Results:

The results from interviews with clinicians showed an explicit desire for AI tools to improve the quality of reports, increase the efficiency of diagnostic procedures, and the significance of the findings by providing additional information. Clinicians stated a reliable performance of results as well as robust and efficient usability as prerequisites for regular use. At the same time, some interviewees discussed the extent to which the use of AI could lead to a reduction of disease categories to measurable data, thereby eclipsing the subjective experience of patients. The focus groups and observational study highlighted emerging changes and challenges in the physician's role and responsibilities. Patients and their relatives showed interest in AI technology and were open to its clinical application. These findings highlight the ethical and social considerations associated with the use of AI tools in clinical practice.

Conclusions:

Digital transformation will change current diagnostic procedures and roles. In TESIComp, we will help to assess the future impact of AI-based approaches in clinical practice and will provide empirically informed ethical recommendations.

Disorders of the Nervous System:

Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)

Education, History and Social Aspects of Brain Imaging:

Education, History and Social Aspects of Brain Imaging 1

Emotion, Motivation and Social Neuroscience:

Social Interaction 2

Modeling and Analysis Methods:

Classification and Predictive Modeling

Novel Imaging Acquisition Methods:

Anatomical MRI

Keywords:

Degenerative Disease
Design and Analysis
Informatics
Machine Learning
Open-Source Code
Open-Source Software
Social Interactions
Statistical Methods
STRUCTURAL MRI
Workflows

1|2Indicates the priority used for review

Abstract Information

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Please indicate below if your study was a "resting state" or "task-activation” study.

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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Patients

Was this research conducted in the United States?

No

Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Yes

Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Structural MRI

For human MRI, what field strength scanner do you use?

1.5T
3.0T

Which processing packages did you use for your study?

SPM
Free Surfer
Other, Please list  -   ANTs; FastSurfer

Provide references using APA citation style.

Dyrba, M. et al. 2021. Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer's disease. Alzheimer’s Research & Therapy 13:191. https://doi.org/10.1186/s13195-021-00924-2

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No