Factors that influence the adoption of human-AI collaboration in clinical decision-making

• URL: http://arxiv.org/abs/2204.09082v1
• Date: Tue, 19 Apr 2022 18:19:39 GMT
• Title: Factors that influence the adoption of human-AI collaboration in clinical decision-making
• Authors: Patrick Hemmer, Max Schemmer, Lara Riefle, Nico Rosellen, Michael V\"ossing, Niklas K\"uhl
• Abstract summary: We identify factors for the adoption of human-AI collaboration by conducting a series of semi-structured interviews with experts in the healthcare domain. We identify six relevant adoption factors and highlight existing tensions between them and effective human-AI collaboration.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent developments in Artificial Intelligence (AI) have fueled the emergence of human-AI collaboration, a setting where AI is a coequal partner. Especially in clinical decision-making, it has the potential to improve treatment quality by assisting overworked medical professionals. Even though research has started to investigate the utilization of AI for clinical decision-making, its potential benefits do not imply its adoption by medical professionals. While several studies have started to analyze adoption criteria from a technical perspective, research providing a human-centered perspective with a focus on AI's potential for becoming a coequal team member in the decision-making process remains limited. Therefore, in this work, we identify factors for the adoption of human-AI collaboration by conducting a series of semi-structured interviews with experts in the healthcare domain. We identify six relevant adoption factors and highlight existing tensions between them and effective human-AI collaboration.

Related papers

• Interactive Example-based Explanations to Improve Health Professionals' Onboarding with AI for Human-AI Collaborative Decision Making [2.964175945467257]
  A growing research explores the usage of AI explanations on user's decision phases for human-AI collaborative decision-making. Previous studies found the issues of overreliance on wrong' AI outputs. We propose interactive example-based explanations to improve health professionals' offboarding with AI.
  arXiv  Detail & Related papers  (2024-09-24T07:20:09Z)
• Emotional Intelligence Through Artificial Intelligence : NLP and Deep Learning in the Analysis of Healthcare Texts [1.9374282535132377]
  This manuscript presents a methodical examination of the utilization of Artificial Intelligence in the assessment of emotions in texts related to healthcare. We scrutinize numerous research studies that employ AI to augment sentiment analysis, categorize emotions, and forecast patient outcomes. There persist challenges, which encompass ensuring the ethical application of AI, safeguarding patient confidentiality, and addressing potential biases in algorithmic procedures.
  arXiv  Detail & Related papers  (2024-03-14T15:58:13Z)
• Beyond Recommender: An Exploratory Study of the Effects of Different AI Roles in AI-Assisted Decision Making [48.179458030691286]
  We examine three AI roles: Recommender, Analyzer, and Devil's Advocate. Our results show each role's distinct strengths and limitations in task performance, reliance appropriateness, and user experience. These insights offer valuable implications for designing AI assistants with adaptive functional roles according to different situations.
  arXiv  Detail & Related papers  (2024-03-04T07:32:28Z)
• Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice [3.595471754135419]
  We show the relationship between learning and appropriate reliance in an experiment with 100 participants. This work provides fundamental concepts for analyzing reliance and derives implications for the effective design of human-AI decision-making.
  arXiv  Detail & Related papers  (2023-10-03T14:51:53Z)
• FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare [73.78776682247187]
  Concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
  arXiv  Detail & Related papers  (2023-08-11T10:49:05Z)
• The Impact of Imperfect XAI on Human-AI Decision-Making [8.305869611846775]
  We evaluate how incorrect explanations influence humans' decision-making behavior in a bird species identification task. Our findings reveal the influence of imperfect XAI and humans' level of expertise on their reliance on AI and human-AI team performance.
  arXiv  Detail & Related papers  (2023-07-25T15:19:36Z)
• The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
  The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
  arXiv  Detail & Related papers  (2022-12-08T23:23:39Z)
• Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging [24.911186503082465]
  This study explores the effects of providing AI assistance at the start of a diagnostic session in radiology versus after the radiologist has made a provisional decision. We found that participants who are asked to register provisional responses in advance of reviewing AI inferences are less likely to agree with the AI regardless of whether the advice is accurate and, in instances of disagreement with the AI, are less likely to seek the second opinion of a colleague.
  arXiv  Detail & Related papers  (2022-05-19T16:59:25Z)
• Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making [46.625616262738404]
  We use knowledge from the field of cognitive science to account for cognitive biases in the human-AI collaborative decision-making setting. We focus specifically on anchoring bias, a bias commonly encountered in human-AI collaboration.
  arXiv  Detail & Related papers  (2020-10-15T22:25:41Z)
• Artificial Artificial Intelligence: Measuring Influence of AI 'Assessments' on Moral Decision-Making [48.66982301902923]
  We examined the effect of feedback from false AI on moral decision-making about donor kidney allocation. We found some evidence that judgments about whether a patient should receive a kidney can be influenced by feedback about participants' own decision-making perceived to be given by AI.
  arXiv  Detail & Related papers  (2020-01-13T14:15:18Z)
• Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making [53.62514158534574]
  We study whether features that reveal case-specific model information can calibrate trust and improve the joint performance of the human and AI. We show that confidence score can help calibrate people's trust in an AI model, but trust calibration alone is not sufficient to improve AI-assisted decision making.
  arXiv  Detail & Related papers  (2020-01-07T15:33:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.