# Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging

arXiv: https://arxiv.org/abs/2205.09696 · HTML: https://mlsanity.com/paper/2205.09696/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 95 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 23 of 23)

- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- [Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer](https://mlsanity.com/paper/1711.06664/) (2017)
- [Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices](https://mlsanity.com/paper/1906.09208/) (2019)
- [On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection](https://mlsanity.com/paper/1811.07901/) (2018)
- [Learning to Complement Humans](https://mlsanity.com/paper/2005.00582/) (2020)
- [To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making](https://mlsanity.com/paper/2102.09692/) (2021)
- [Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making](https://mlsanity.com/paper/1902.02960/) (2019)
- [The Algorithmic Automation Problem: Prediction, Triage, and Human Effort](https://mlsanity.com/paper/1903.12220/) (2019)
- [Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening](https://mlsanity.com/paper/1903.08297/) (2019)
- [A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores](https://mlsanity.com/paper/2002.08035/) (2020)
- [Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making](https://mlsanity.com/paper/2010.07938/) (2020)
- [Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning](https://mlsanity.com/paper/2202.05402/) (2022)
- [Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier](https://mlsanity.com/paper/1706.04152/) (2017)
- [Detecting and classifying lesions in mammograms with Deep Learning](https://mlsanity.com/paper/1707.08401/) (2017)
- ["The Human Body is a Black Box": Supporting Clinical Decision-Making with Deep Learning](https://mlsanity.com/paper/1911.08089/) (2019)
- [Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens](https://mlsanity.com/paper/2102.00593/) (2021)
- [Learning Representations by Humans, for Humans](https://mlsanity.com/paper/1905.12686/) (2019)
- [The Impact of Algorithmic Risk Assessments on Human Predictions and its Analysis via Crowdsourcing Studies](https://mlsanity.com/paper/2109.01443/) (2021)
- [Heterogeneity in Algorithm-Assisted Decision-Making: A Case Study in Child Abuse Hotline Screening](https://mlsanity.com/paper/2204.05478/) (2022)
- [RapidRead: Global Deployment of State-of-the-art Radiology AI for a Large Veterinary Teleradiology Practice](https://mlsanity.com/paper/2111.08165/) (2021)

## Built on by (most cited first, 30 of 32)

- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [Harms from Increasingly Agentic Algorithmic Systems](https://mlsanity.com/paper/2302.10329/) (2023)
- [Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making](https://mlsanity.com/paper/2301.05809/) (2023)
- ["Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction](https://mlsanity.com/paper/2210.03735/) (2022)
- [Determinants of LLM-assisted Decision-Making](https://mlsanity.com/paper/2402.17385/) (2024)
- [Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Making](https://mlsanity.com/paper/2208.07960/) (2022)
- [Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis](https://mlsanity.com/paper/2309.12368/) (2023)
- [The Typing Cure: Experiences with Large Language Model Chatbots for Mental Health Support](https://mlsanity.com/paper/2401.14362/) (2024)
- [Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review](https://mlsanity.com/paper/2310.19778/) (2023)
- [The Rise of the AI Co-Pilot: Lessons for Design from Aviation and Beyond](https://mlsanity.com/paper/2311.14713/) (2023)
- [NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMs](https://mlsanity.com/paper/2312.11803/) (2023)
- [Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills](https://mlsanity.com/paper/2410.04253/) (2024)
- [One vs. Many: Comprehending Accurate Information from Multiple Erroneous and Inconsistent AI Generations](https://mlsanity.com/paper/2405.05581/) (2024)
- [The Road to Artificial SuperIntelligence: A Comprehensive Survey of Superalignment](https://mlsanity.com/paper/2412.16468/) (2024)
- [Algorithmic Assistance with Recommendation-Dependent Preferences](https://mlsanity.com/paper/2208.07626/) (2022)
- [Decoding AI's Nudge: A Unified Framework to Predict Human Behavior in AI-assisted Decision Making](https://mlsanity.com/paper/2401.05840/) (2024)
- [A Survey of AI Reliance](https://mlsanity.com/paper/2408.03948/) (2024)
- [Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting](https://mlsanity.com/paper/2410.12284/) (2024)
- [When Are Two Lists Better than One?: Benefits and Harms in Joint Decision-making](https://mlsanity.com/paper/2308.11721/) (2023)
- [From Evidence to Decision: Exploring Evaluative AI](https://mlsanity.com/paper/2402.01292/) (2024)
- [Beyond Recommendations: From Backward to Forward AI Support of Pilots' Decision-Making Process](https://mlsanity.com/paper/2406.08959/) (2024)
- [Multi-Round Human-AI Collaboration with User-Specified Requirements](https://mlsanity.com/paper/2602.17646/) (2026)
- [Can AI Explanations Make You Change Your Mind?](https://mlsanity.com/paper/2508.08158/) (2025)
- [Explainable AI Enhances Glaucoma Referrals, Yet the Human-AI Team Still Falls Short of the AI Alone](https://mlsanity.com/paper/2407.11974/) (2024)
- ["It depends": Configuring AI to Improve Clinical Usefulness Across Contexts](https://mlsanity.com/paper/2407.11978/) (2024)
- [Exploring the Impact of Explainable AI and Cognitive Capabilities on Users' Decisions](https://mlsanity.com/paper/2505.01192/) (2025)
- [The Impact of AI Usage and Informativeness on Skill Development in Logical Reasoning](https://mlsanity.com/paper/2605.21695/) (2026)
- [Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making](https://mlsanity.com/paper/2602.11924/) (2026)
- [Human-AI Collaboration in Large Language Model-Integrated Building Energy Management Systems: The Role of User Domain Knowledge and AI Literacy](https://mlsanity.com/paper/2602.16140/) (2026)
- [Not All Trust is the Same: Effects of Decision Workflow and Explanations in Human-AI Decision Making](https://mlsanity.com/paper/2603.05229/) (2026)

## Abstract (arXiv)

Details of the designs and mechanisms in support of human-AI collaboration must be considered in the real-world fielding of AI technologies. A critical aspect of interaction design for AI-assisted human decision making are policies about the display and sequencing of AI inferences within larger decision-making workflows. We have a poor understanding of the influences of making AI inferences available before versus after human review of a diagnostic task at hand. We explore 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 conducted a user study where 19 veterinary radiologists identified radiographic findings present in patients' X-ray images, with the aid of an AI tool. We employed two workflow configurations to analyze (i) anchoring effects, (ii) human-AI team diagnostic performance and agreement, (iii) time spent and confidence in decision making, and (iv) perceived usefulness of the AI. 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. These participants also reported the AI advice to be less useful. Surprisingly, requiring provisional decisions on cases in advance of the display of AI inferences did not lengthen the time participants spent on the task. The study provides generalizable and actionable insights for the deployment of clinical AI tools in human-in-the-loop systems and introduces a methodology for studying alternative designs for human-AI collaboration. We make our experimental platform available as open source to facilitate future research on the influence of alternate designs on human-AI workflows.