Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS)

By Wenjuan Fan, Jingnan Liu, Shuwan Zhu, P. Pardalos. Annals of Operations Research, 2018

308 citations recorded by the source corpus.

Subfields: Information Systems and Management, Management Information Systems, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/4f0b88b6da27b9c4001f3c780d34d3f1ee4c895c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems and Management

1. Ahmad Samed Al-Adwan
2. Ghazanfar Ali Abbasi
3. Dino Pedreschi
4. Paul Rodway
5. Astrid Schepman
6. Akhmad Habibi

In Management Information Systems

1. Dino Pedreschi

In Organizational Behavior and Human Resource Management

1. Ahmad Samed Al-Adwan
2. Paul Rodway
3. Astrid Schepman

Citations in the corpus

• Trustworthy Artificial Intelligence: A Review (2022, background): “For example, the adoption of AI-based medical diagnosis support systems among healthcare professionals is relatively low because of its uninterpretability even though it can be beneficial in day-to-day clinical practices [62].”
• The Roles of Personality Traits, AI Anxiety, and Demographic Factors in Attitudes toward Artificial Intelligence (2022, background): “AI technologies cover a wide area, such as the economy (Belanche et al., 2019), education (Chocarro et al., 2021), health (Fan et al., 2020), law (Xu & Wang, 2021), transportation (Ribeiro et al., 2022), agriculture (Mohr & K € uhl, 2021), and tourism (Go et al., 2020).”
• “Extending the Technology Acceptance Model (TAM) to Predict University Students’ Intentions to Use Metaverse-Based Learning Platforms” (2023, background): “Personal innovativeness (PI) is the desire to try out new innovations (Fan et al., 2020).”
• Doctor XAI: an ontology-based approach to black-box sequential data classification explanations (2020, background): “Furthermore, being able to understand the reasoning behind the model’s predictions would increase the healthcare professionals’ trust in such a technology and would increase its acceptance and use [14].”
• Outlining the Design Space of Explainable Intelligent Systems for Medical Diagnosis (2019, background): “A study has been made to know doctors’ acceptance and the adopt intention of these systems [15].”