Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians
By Onur Asan, Alparslan Emrah Bayrak, Avishek Choudhury. Journal of Medical Internet Research, 2019
657 citations recorded by the source corpus.
Artificial intelligence (AI) can transform health care practices with its increasing ability to translate the uncertainty and complexity in data into actionable—though imperfect—clinical decisions or suggestions. In the evolving relationship between humans and AI, trust is the one mechanism that shapes clinicians’ use and adoption of AI. Trust is a psychological mechanism to deal with the uncertainty between what is known and unknown. Several research studies have highlighted the need for improving AI-based systems and enhancing their capabilities to help clinicians. However, assessing the magnitude and impact of human trust on AI technology demands substantial attention. Will a clinician trust an AI-based system? What are the factors that influence human trust in AI? Can trust in AI be optimized to improve decision-making processes? In this paper, we focus on clinicians as the primary users of AI systems in health care and present factors shaping trust between clinicians and AI. We highlight critical challenges related to trust that should be considered during the development of any AI system for clinical use.
Subfields: Family Practice, General Health Professions, Health Informatics
View Paper: https://www.semanticscholar.org/paper/77f6a75bba74f36699edceb0edb107dcfb1aaaa1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Family Practice
In General Health Professions
In Health Informatics
- Saleh Afroogh
- Ali Akbari
- Mohammadali Kargar
- Emmie Malone
- Paul Rodway
- Astrid Schepman
- Emilio Ferrara
- Hananeh Alambeigi
Citations in the corpus
- Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden (2022, background): “The conclusion in newly published reviews addressing regulation, privacy and legal aspects [15, 16], ethics [16–18], clinical and patient outcomes [19–21] and economic impact [22], is that further research is needed in a real-world clinical setting although the clinical implementation of AI…”
- SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification (2021, background): “Although it is not the goal that machine intelligence will entirely replace human sleep experts [2]–[4], we envision it could work alongside and assist human experts to facilitate their jobs and scale up sleep assessment and diagnosis.”
- Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies (2023, background): “Researchers and practitioners have proposed various mitigation strategies, such as improving data quality [8] and designing explicitly fair algorithms [9–11].”
- Trust in AI: progress, challenges, and future directions (2024, positive): “6, where the former could cause damages and the latter leads to less adoption of the AI systems (Asan et al., 2020; Alambeigi et al., 2021).”
- The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory Validation and Associations with Personality, Corporate Distrust, and General Trust (2022, background): “Similarly, AI used in health settings raises issues of confidence around its performance (Asan et al., 2020; Baldauf et al., 2020).”