Patients’ views on the use of artificial intelligence in healthcare: Artificial Intelligence Survey Aachen (AISA)-a prospective survey
This study explores how patients feel about using artificial intelligence (AI) in healthcare. It found that most patients expect AI to be beneficial, especially in diagnosing and treating health issues, but many feel they don't know much about AI.
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- 1 Supporting this hypothesis, 72.6% of patients who supported general AI use rejected its use in triage.
- 2 Further studies could investigate whether objective or subjective AI knowledge increases acceptance.
- 3 The majority wished to be informed when AI was used in diagnostics, treatment decision making, and procedure support.
- 4 Explainable AI (xAI) aims to explain the information behind deep learning models to reveal how decisions are made.
Introduction
Artificial Intelligence (AI) permeates many areas of modern life, including healthcare. Medical AI applications range from radiological assistant systems and diagnostic tools to algorithms for psychiatric disorders.
Some view AI as a beacon of hope for healthcare, while others express concerns about its rapid spread into clinical routine.
A discrepancy exists between fast technical advances in AI and patients’ acceptance of AI.
Our single-site design and relatively small sample size limit the generalizability of our findings.
Research Question
Supporting this hypothesis, 72.6% of patients who supported general AI use rejected its use in triage. Further studies could investigate whether objective or subjective AI knowledge increases acceptance.
Methodology
Thirty-seven participants completed the preliminary questionnaire, and 161 completed the updated questionnaire. We provided information on the purpose and expected time to participants and presented the questionnaire in German.
Study Design
We completed a preliminary test run with an item analysis after 37 participants.
We conducted a binary logistic regression to identify factors impacting AI approval, including age, gender, and AI knowledge.
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Results & Findings
Previous studies on specific use cases limit the generalizability of results to the general patient population. We conducted our survey at the University Hospital RWTH Aachen from December 2022 to October 2023 after ethics approval and informed consent.
- Previous studies on specific use cases limit the generalizability of results to the general patient population.
- We conducted our survey at the University Hospital RWTH Aachen from December 2022 to October 2023 after ethics approval and informed consent.
- We modified the questionnaire by removing a response option, omitting two items, and adding two additional items.
- We calculated correlations using Spearman’s correlation coefficient.
- We dichotomised item 4 into acceptance versus neutral and negative attitudes and defined p-values < 0.05 as significant.
Previous studies on specific use cases limit the generalizability of results to the general patient population.
The majority wished to be informed when AI was used in diagnostics, treatment decision making, and procedure support.
Practical Applications
We excluded patients receiving acute treatment that could be delayed by study participation. The rejection of AI in triage may be because triage is associated with fear and uncertainty.
Uncertainty towards triage mechanisms combined with uncertainty towards AI may amplify disapproval in this field.
Resistance to medical AI may be caused by algorithm aversion and an illusory understanding of human decision-making.
Statistical analysis
Descriptive statistics were used to analyze the data, with various tests applied to identify factors influencing patients’ approval of AI. The analysis included demographic variables and self-assessed knowledge of AI.
Figures Explained
The paper’s visual material highlights the workflow and the main system components.
- Figure 1.
- Figure 2.
Frequently Asked Questions
Supporting this hypothesis, 72.6% of patients who supported general AI use rejected its use in triage. Further studies could investigate whether objective or subjective AI knowledge increases acceptance.
Multivariable regression analyses showed no variables independently associated with approval for AI in diagnostics or triage. Our results indicated that most participants approved AI as a support system for doctors rather than a substitute.
The majority wished to be informed when AI was used in diagnostics, treatment decision making, and procedure support. Explainable AI (xAI) aims to explain the information behind deep learning models to reveal how decisions are made.
The rejection of AI in triage may be because triage is associated with fear and uncertainty. Rapid AI developments mean our study is only a momentary picture, and patients’ views may change quickly.
Previous studies on specific use cases limit the generalizability of results to the general patient population. Our single-site design and relatively small sample size limit the generalizability of our findings.
This study explores how patients feel about using artificial intelligence (AI) in healthcare. It found that most patients expect AI to be beneficial, especially in diagnosing and treating health issues, but many feel they don’t know much about AI.