CheXplain: Enabling Physicians to Explore and Understand Data-Driven, AI-Enabled Medical Imaging Analysis

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370 · see citing works

Explanation in artificial intelligence: Insights from the social sciences

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Artificial intelligence in healthcare: past, present and future

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VQA: Visual Question Answering

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DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

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A theory of diagnosis from first principles

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Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission

Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2788613 · Cited by 1,685 · see citing works

The Inevitable Application of Big Data to Health Care

Murdoch, T. B., & Detsky, A. S. (2013) · JAMA · doi:10.1001/jama.2013.393 · Cited by 1,676 · see citing works

Interpretable Explanations of Black Boxes by Meaningful Perturbation

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Designing Theory-Driven User-Centric Explainable AI

Wang, D., Yang, Q., Abdul, A., & Lim, B. Y. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300831 · Cited by 906 · see citing works

Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction

Park, S. H., & Han, K. (2018) · Radiology · doi:10.1148/radiol.2017171920 · Cited by 894 · see citing works

Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1323 · Cited by 829 · see citing works

Artificial intelligence in medical practice: the question to the answer?

Miller, D. D., & Brown, E. W. (2018) · The American Journal of Medicine · doi:10.1016/j.amjmed.2017.10.035 · Cited by 774 · see citing works

Accountability in algorithmic decision making

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Grad-CAM: Why did you say that?Preprint

Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., & Batra, D. (2016) · arXiv.org · doi:10.48550/arxiv.1611.07450 · Cited by 646 · see citing works

Adapting to Artificial Intelligence: Radiologists and Pathologists as Information Specialists

Jha, S., & Topol, E. J. (2016) · JAMA · doi:10.1001/jama.2016.17438 · Cited by 622 · see citing works

Generating Visual Explanations

Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., & Darrell, T. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_1 · Cited by 534 · see citing works

Intelligibility and Accountability: Human Considerations in Context-Aware Systems

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The Role of Explanations on Trust and Reliance in Clinical Decision Support Systems

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Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making

Cai, C. J., Reif, E., Hegde, N., Hipp, J., Kim, B., Smilkov, D., Wattenberg, M., Viegas, F., Corrado, G. S., Stumpe, M. C., et al. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300234 · Cited by 405 · see citing works

CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301590 · Cited by 392 · see citing works

Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation

Shin, H.-C., Roberts, K. E., Lü, L., Demner‐Fushman, D., Yao, J., & Summers, R. M. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.274 · Cited by 390 · see citing works

Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models

Krause, J., Perer, A., & Ng, K. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858529 · Cited by 390 · see citing works

Causal–explanatory pluralism: How intentions, functions, and mechanisms influence causal ascriptions

Lombrozo, T. (2010) · Cognitive Psychology · doi:10.1016/j.cogpsych.2010.05.002 · Cited by 315 · see citing works

Functional explanation and the function of explanation

Lombrozo, T., & Carey, S. E. (2005) · Cognition · doi:10.1016/j.cognition.2004.12.009 · Cited by 301 · see citing works

DXplain

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Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges

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The Radiology Report as Seen by Radiologists and Referring Clinicians: Results of the COVER and ROVER Surveys

Bosmans, J. M. L., Weyler, J., de Schepper, A. M., & Parizel, P. M. (2011) · Radiology · doi:10.1148/radiol.10101045 · Cited by 238 · see citing works

Improving Interpretability of Deep Neural Networks with Semantic Information

Dong, Y., Su, H., Zhu, J., & Zhang, B. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.110 · Cited by 141 · see citing works

Radiology reporting: attitudes of referring physicians.

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ToolClips

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Why CAD Failed in Mammography

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Developing a reflective model of collaborative systems

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Toward a Geographic Understanding of the Sharing Economy

Thebault-Spieker, J., Terveen, L., & Hecht, B. J. (2017) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3058499 · Cited by 101 · see citing works

Argumentation-Based Inference and Decision Making--A Medical Perspective

Fox, J., Glasspool, D. W., Grecu, D. L., Modgil, S., South, M., & Patkar, V. (2007) · IEEE Intelligent Systems · doi:10.1109/mis.2007.102 · Cited by 87 · see citing works

A security architecture for interconnecting health information systems

Gritzalis, D., & Lambrinoudakis, C. (2004) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2003.12.011 · Cited by 85 · see citing works

Communicating Results of All Radiologic Examinations Directly to Patients: Has the Time Come?

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Generating Counterfactual Explanations with Natural LanguagePreprint

Hendricks, L. A., Hu, R., Darrell, T., & Akata, Z. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.09809 · Cited by 81 · see citing works

The dangers of faulty, biased, or malicious algorithms requires independent oversight

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Attentive Explanations: Justifying Decisions and Pointing to the EvidencePreprint

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Radiology reporting in an academic children's hospital: what referring physicians think

Gunderman, R. B., Ambrosius, W. T., & Cohen, M. D. (2000) · Pediatric Radiology · doi:10.1007/s002470050746 · Cited by 51 · see citing works

EDITORIALS

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Detection of pulmonary nodules on chest x-ray images using R-CNN

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Pitfalls of the Vague Radiology Report

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Improving communications between team physician and radiologist

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Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review

Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021) · Applied Sciences · doi:10.3390/app11115088 · Cited by 629 · see citing works

Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review

Chen, H., Gomez, C., Huang, C.-M., & Unberath, M. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00699-2 · Cited by 300 · see citing works

Explainable AI in medical imaging: An overview for clinical practitioners – Saliency-based XAI approaches

Borys, K., Schmitt, Y. A., Nauta, M., Seifert, C., Krämer, N. C., Friedrich, C. M., & Nensa, F. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110787 · Cited by 178 · see citing works

Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens

Jacobs, M., He, J., F. Pradier, M., Lam, B., Ahn, A. C., McCoy, T. H., Perlis, R. H., Doshi-Velez, F., & Gajos, K. Z. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445385 · Cited by 177 · see citing works

Understanding the impact of explanations on advice-taking: a user study for AI-based clinical Decision Support Systems

Panigutti, C., Beretta, A., Giannotti, F., & Pedreschi, D. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502104 · Cited by 145 · see citing works

BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions

Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102285 · Cited by 125 · see citing works

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Kaluarachchi, T. I., Reis, A., & Nanayakkara, S. C. (2021) · Sensors · doi:10.3390/s21072514 · Cited by 132 · see citing works

Human-XAI Interaction: A Review and Design Principles for Explanation User Interfaces

Chromik, M., & Butz, A. M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85616-8_36 · Cited by 128 · see citing works

Assertiveness-based Agent Communication for a Personalized Medicine on Medical Imaging Diagnosis

Calisto, F. M., Fernandes, J., Morais, M., Santiago, C., Abrantes, J., Nunes, N., & Nascimento, J. C. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580682 · Cited by 106 · see citing works

“It cannot do all of my work”: Community Health Worker Perceptions of AI-Enabled Mobile Health Applications in Rural India

Okolo, C. T., Kamath, S., Dell, N. L., & Vashistha, A. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445420 · Cited by 96 · see citing works

Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI

Ehsan, U., Saha, K., Choudhury, M. D., & Riedl, M. O. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579467 · Cited by 92 · see citing works

Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology

Yildirim, N., Richardson, H., Wetscherek, M. T., Bajwa, J., Jacob, J., Pinnock, M. A., Harris, S., Coelho De Castro, D., Bannur, S., Hyland, S., et al. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642013 · Cited by 82 · see citing works

XAIR: A Framework of Explainable AI in Augmented Reality

Xu, X., Yu, A., Jonker, T. R., Todi, K., Lu, F., Qian, X., Belo, J. M. E., Wang, T., Li, M., Mun, A., et al. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581500 · Cited by 84 · see citing works

A Survey of Human‐Centered Evaluations in Human‐Centered Machine Learning

Sperrle, F., El‐Assady, M., Guo, G., Borgo, R., Chau, D. H., Endert, A., & Keim, D. A. (2021) · Computer Graphics Forum · doi:10.1111/cgf.14329 · Cited by 79 · see citing works

Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems

Yang, Q., Hao, Y., Quan, K., Yang, S., Zhao, Y., Kuleshov, V., & Wang, F. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581393 · Cited by 75 · see citing works

Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation

Zhang, A., Walker, O., Nguyen, K., Dai, J., Chen, A., & Lee, M. K. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579601 · Cited by 77 · see citing works

X-MIR: EXplainable Medical Image Retrieval

Hu, B., Vasu, B., & Hoogs, A. J. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00161 · Cited by 68 · see citing works

Do Humans Trust Advice More if it Comes from AI?: An Analysis of Human-AI Interactions

Vodrahalli, K., Daneshjou, R., Gerstenberg, T., & Zou, J. (2022) · Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3514094.3534150 · Cited by 63 · see citing works

Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI

Liao, Q. V., Zhang, Y., Luss, R., Doshi-Velez, F., & Dhurandhar, A. (2022) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v10i1.21995 · Cited by 69 · see citing works

Designing explainable AI to improve human-AI team performance: A medical stakeholder-driven scoping review

Subramanian, H. V., Canfield, C., & Shank, D. B. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102780 · Cited by 67 · see citing works

Lessons Learned from Designing an AI-Enabled Diagnosis Tool for Pathologists

Gu, H., Huang, J., Hung, L., & Chen, X. '. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449084 · Cited by 56 · see citing works

Fair Conformal Predictors for Applications in Medical Imaging

Lu, C., Lemay, A., Chang, K., Hoebel, K., & Kalpathy–Cramer, J. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i11.21459 · Cited by 58 · see citing works

Explainable Machine Learning for Lung Cancer Screening Models

Kobylińska, K., Orlowski, T. M., Adamek, M., & Biecek, P. (2022) · Applied Sciences · doi:10.3390/app12041926 · Cited by 45 · see citing works

Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making

Ma, S., Chen, Q., Wang, X., Zheng, C., Peng, Z., Yin, M., & Ma, X. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713423 · Cited by 48 · see citing works

A novel explainable neural network for Alzheimer's disease diagnosis

Yu, L., Xiang, W., Fang, J., Chen, Y.-P. P., & Zhu, R. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108876 · Cited by 44 · see citing works

Personalized explanations for clinician-AI interaction in breast imaging diagnosis by adapting communication to expertise levels

Calisto, F. M., Abrantes, J., Santiago, C., Nunes, N., & Nascimento, J. C. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103444 · Cited by 40 · see citing works

The methodology of studying fairness perceptions in Artificial Intelligence: Contrasting CHI and FAccT

van Berkel, N., Sarsenbayeva, Z., & Gonçalves, J. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102954 · Cited by 41 · see citing works

Seamful XAI: Operationalizing Seamful Design in Explainable AI

Ehsan, U., Liao, Q. V., Passi, S., Riedl, M. O., & Daumé, H. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637396 · Cited by 61 · see citing works

Introduction to the Special Issue on Human-Centred AI in Healthcare: Challenges Appearing in the Wild

Andersen, T. O., Nunes, F., Wilcox, L. G., Coiera, E. W., & Rogers, Y. (2023) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3589961 · Cited by 44 · see citing works

A Clinical Decision Support System for Sleep Staging Tasks With Explanations From Artificial Intelligence: User-Centered Design and Evaluation Study

Hwang, J., Lee, T., Lee, H., & Byun, S. (2021) · Journal of Medical Internet Research · doi:10.2196/28659 · Cited by 39 · see citing works

Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial

Thirunavukarasu, A. J., Elangovan, K., Gutiérrez, L., Li, Y., Tan, I., Keane, P. A., Korot, E., & Ting, D. S. W. (2023) · Journal of Medical Internet Research · doi:10.2196/49949 · Cited by 41 · see citing works

The Entoptic Field Camera as Metaphor-Driven Research-through-Design with AI Technologies

Benjamin, J. J., Biggs, H. R., Berger, A., Rukanskaitė, J., Heidt, M., Merrill, N., Pierce, J., & Lindley, J. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581175 · Cited by 36 · see citing works

A Roadmap of Explainable Artificial Intelligence: Explain to Whom, When, What and How?

Wang, Z., Huang, C., & Yao, X. (2024) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3702004 · Cited by 32 · see citing works

Deep-spindle: An automated sleep spindle detection system for analysis of infant sleep spindles

Wei, L., Ventura, S., Ryan, M. A., Mathieson, S., Boylan, G. B., Lowery, M. M., & Mooney, C. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106096 · Cited by 29 · see citing works

Explainable AI decision support improves accuracy during telehealth strep throat screening

Gomez, C., Smith, B.-L., Zayas, A., Unberath, M., & Canares, T. (2024) · Communications Medicine · doi:10.1038/s43856-024-00568-x · Cited by 30 · see citing works

Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images

Fontanella, A., Mair, G., Wardlaw, J. M., Trucco, E., & Storkey, A. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3460391 · Cited by 29 · see citing works

Toward Human-centered XAI in Practice: A survey

Kong, X., Liu, S., & Zhu, L. (2024) · Machine Intelligence Research · doi:10.1007/s11633-022-1407-3 · Cited by 31 · see citing works

"What is Your Envisioned Future?": Toward Human-AI Enrichment in Data Work of Asthma Care

Su, Z., He, L., Jariwala, S. P., Zheng, K., & Chen, Y. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555157 · Cited by 27 · see citing works

An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph

Pham, H., Nguyen, H. Q., Lam, K., Le, L. T., Nguyen, D. B., Nguyen, H. T., Le, T. T., Nguyen, T. V., Dao, M., & Vu, V. (2022) · IEEE Access · doi:10.1109/access.2022.3210468 · Cited by 26 · see citing works

AI-Enabled Data-Driven Approaches for Personalized Medicine and Healthcare Analytics

Mendhe, D., Dogra, A., Nair, P. S., Punitha, S. C., Preetha, K. S., & Babu, S. B. G. T. (2024) · International Conference on Science Technology Engineering and Mathematics (ICONSTEM) · doi:10.1109/iconstem60960.2024.10568722 · Cited by 24 · see citing works

How can Explainability Methods be Used to Support Bug Identification in Computer Vision Models?

Balayn, A., Rikalo, N., Lofi, C., Yang, J., & Bozzon, A. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517474 · Cited by 24 · see citing works

``It Is a Moving Process": Understanding the Evolution of Explainability Needs of Clinicians in Pulmonary Medicine

Corti, L., Oltmans, R., Jung, J., Balayn, A., Wijsenbeek, M. S., & Yang, J. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642551 · Cited by 24 · see citing works

Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs

Suresh, H., Lewis, K. M., Guttag, J. V., & Satyanarayan, A. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511160 · Cited by 22 · see citing works

How Can We Develop Explainable Systems? Insights from a Literature Review and an Interview Study

Chazette, L., Klünder, J., Balci, M., & Schneider, K. A. (2022) · doi:10.1145/3529320.3529321 · Cited by 21 · see citing works

Investigation of a Web-Based Explainable AI Screening for Prolonged Grief Disorder

She, W. J., Ang, C. S., Neimeyer, R. A., Burke, L. A., Zhang, Y., Jatowt, A., Kawai, Y., Hu, J., Rauterberg, M., Prigerson, H. G., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3163311 · Cited by 25 · see citing works

"If it is easy to understand then it will have value": Examining Perceptions of Explainable AI with Community Health Workers in Rural India

Okolo, C. T., Agarwal, D., Dell, N. L., & Vashistha, A. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637348 · Cited by 21 · see citing works

Explanation Strategies as an Empirical-Analytical Lens for Socio-Technical Contextualization of Machine Learning Interpretability

Benjamin, J. J., Kinkeldey, C., Müller-Birn, C., Korjakow, T., & Herbst, E.-M. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3492858 · Cited by 19 · see citing works

EyeXplain Autism: Interactive System for Eye Tracking Data Analysis and Deep Neural Network Interpretation for Autism Spectrum Disorder Diagnosis

de Belen, R. A. J., Bednarz, T. P., & Sowmya, A. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3451784 · Cited by 19 · see citing works

Comparing Zealous and Restrained AI Recommendations in a Real-World Human-AI Collaboration Task

Xu, C., Lien, K.-C., & Höllerer, T. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581282 · Cited by 18 · see citing works

XAlgo: a Design Probe of Explaining Algorithms’ Internal States via Question-Answering

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Use of machine learning to identify patients at risk of sub-optimal adherence: study based on real-world data from 10,929 children using a connected auto-injector device

Spataru, A., van Dommelen, P., Arnaud, L., Masne, Q. L., Quarteroni, S., & Koledova, E. B. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01918-2 · Cited by 18 · see citing works

Question-Driven Design Process for Explainable AI User Experiences

Liao, Q. V., Pribić, M., Han, J., Miller, S., & Sow, D. (2021) · arXiv · doi:10.48550/arxiv.2104.03483 · Cited by 18 · see citing works

Trustworthy machine learning for health care

Pandl, K. D., Feiland, F., Thiebes, S., & Sunyaev, A. (2021) · ACM Conference on Health, Inference, and Learning (CHIL) · doi:10.1145/3450439.3451861 · Cited by 16 · see citing works

Artificial Intelligence in Ischemic Heart Disease Prevention

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What Your Radiologist Might be Missing: Using Machine Learning to Identify Mislabeled Instances of X-ray Images

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The human-AI relationship in decision-making: AI explanation to support people on justifying their decisionsPreprint

Ferreira, J. J., & Monteiro, M. D. S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.05460 · Cited by 15 · see citing works

How do People Perceive the Role of AI in Human-AI Collaboration to Solve Everyday Tasks?

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Knowledge Workers' Perspectives on AI Training for Responsible AI Use

Zhang, A., & Lee, M. K. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714100 · Cited by 17 · see citing works

3DPFIX: Improving Remote Novices' 3D Printing Troubleshooting through Human-AI Collaboration Design

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MiMICRI: Towards Domain-centered Counterfactual Explanations of Cardiovascular Image Classification Models

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Radiology Report Annotation Using Generative Large Language Models: Comparative Analysis

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Amplifying Human Capabilities in Prostate Cancer Diagnosis: An Empirical Study of Current Practices and AI Potentials in Radiology

Saßmannshausen, S. M., Ontika, N. N., de Carvalho, A. F. P., Rouncefield, M., & Pipek, V. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642362 · Cited by 13 · see citing works

"It depends": Configuring AI to Improve Clinical Usefulness Across Contexts

Zając, H. D., Ribeiro, J., Ingala, S., Gentile, S., Wanjohi, R., Gitau, S. N., Carlsen, J. F., Nielsen, M. B., & Andersen, T. O. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3660707 · Cited by 14 · see citing works

Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions

Vodrahalli, K., Daneshjou, R., Gerstenberg, T., & Zou, J. (2021) · arXiv · doi:10.48550/arxiv.2107.07015 · Cited by 16 · see citing works

ContractMind: Trust-calibration interaction design for AI contract review tools

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Advancing Problem-Based Learning with Clinical Reasoning for Improved Differential Diagnosis in Medical Education

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Follow the Successful Herd: Towards Explanations for Improved Use and Mental Models of Natural Language Systems

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Transparency in the Wild: Navigating Transparency in a Deployed AI System to Broaden Need-Finding Approaches

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Adjusting Amount of AI Explanation for Visual Tasks

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Towards Clinically Useful AI: From Radiology Practices in Global South and North to Visions of AI Support

Zając, H. D., Andersen, T. O., Kwasa, E., Wanjohi, R., Onyinkwa, M. K., Mwaniki, E. K., Gitau, S. N., Yaseen, S. S., Carlsen, J. F., Fraccaro, M., et al. (2025) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3715115 · Cited by 12 · see citing works

Explainable emphysema detection on chest radiographs with deep learning

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Trust in Clinical AI: Expanding the Unit of Analysis

Browne, J., Bakker, S., Yu, B., Lloyd, P. A., & Allouch, S. B. (2022) · Frontiers in artificial intelligence and applications · doi:10.3233/faia220192 · Cited by 10 · see citing works

Faulty or Ready? Handling Failures in Deep-Learning Computer Vision Models until Deployment: A Study of Practices, Challenges, and Needs

Balayn, A., Rikalo, N., Yang, J., & Bozzon, A. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581555 · Cited by 10 · see citing works

Feature Importance Measures as Explanation for Classification Applied to Hospital Readmission Prediction

Magboo, M. S. A., & Magboo, V. P. C. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.195 · Cited by 10 · see citing works

AI-CDSS Design Guidelines and Practice Verification

He, X., Xi, Z., Ding, H., Liu, Y., & Zhu, H. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2235882 · Cited by 9 · see citing works

Enabling Multi-modal Conversational Interface for Clinical Imaging

Dayanandan, K., & Lall, B. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3650805 · Cited by 9 · see citing works

"I'd be watching him contour till 10 o'clock at night": Understanding Tensions between Teaching Methods and Learning Needs in Healthcare Apprenticeship

Yarmand, M., Chen, C., Cheng, K., Murphy, J. D., & Weibel, N. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642453 · Cited by 9 · see citing works

Meaningful Transparency for Clinicians: Operationalising HCXAI Research with Gynaecologists

Schor, B. G. S., Kallina, E., Singh, J. P., & Blackwell, A. F. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658971 · Cited by 10 · see citing works

Exploring Algorithmic Explainability: Generating Explainable AI Insights for Personalized Clinical Decision Support Focused on Cannabis Intoxication in Young Adults

Zhang, T., Chung, T. A., Dey, A. K., & Bae, S. W. (2024) · International Conference on Activity and Behavior Computing (ABC) · doi:10.1109/abc61795.2024.10652070 · Cited by 10 · see citing works

Visual-Conversational Interface for Evidence-Based Explanation of Diabetes Risk Prediction

Samimi, R., Bhattacharya, A., Gosak, L., Štiglic, G., & Verbert, K. (2025) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3719160.3736616 · Cited by 10 · see citing works

General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology

Makhlouf, Y., Salto‐Tellez, M., James, J. A., O’Reilly, P. G., & Maxwell, P. (2022) · Diagnostics · doi:10.3390/diagnostics12051272 · Cited by 9 · see citing works

Understanding the User Perception and Experience of Interactive Algorithmic Recourse Customization

Koh, S., Kim, B. H., & Jo, S. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3674503 · Cited by 8 · see citing works

Generating Descriptive Explanations of Machine Learning Models Using LLM

Pang, A., Jang, H., & Fang, S. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825667 · Cited by 8 · see citing works

The Situated, Relational, and Evolving Nature of Epilepsy Diagnosis

Marathe, M., & Toyama, K. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3432916 · Cited by 8 · see citing works

ScrutinAI: A visual analytics tool supporting semantic assessments of object detection models

Haedecke, E., Möck, M., & Akila, M. (2023) · Computers & Graphics · doi:10.1016/j.cag.2023.06.010 · Cited by 8 · see citing works

Towards Human-Ai Deliberation: Design and Evaluation of Llm-Empowered Deliberative Ai for Ai-Assisted Decision-MakingPreprint

Ma, S., Chen, Q., Wang, X., Zheng, C., Peng, Z., Yin, M., & Ma, X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4772689 · Cited by 8 · see citing works

Revolutionizing Neurosurgery and Neurology: The transformative impact of artificial intelligence in healthcare

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To Recommend or Not to Recommend: Designing and Evaluating AI-Enabled Decision Support for Time-Critical Medical Events

Mastrianni, A., Kim, M. S., Sullivan, T. M., Sippel, G. J., Burd, R. S., Gajos, K. Z., & Sarcevic, A. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757512 · Cited by 6 · see citing works

MR Microsurgical Suture Training System with Level-Appropriate Support

Tashiro, Y., Miyafuji, S., Kojima, Y., Kiyofuji, S., Kin, T., Igarashi, T., & Koike, H. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642324 · Cited by 6 · see citing works

Design of explanation user interfaces for interactive machine learning using the example of a knowledge graph-based approach to explainable process analysis

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OPG-SHAP: A Dental AI Tool for Explaining Learned Orthopantomogram Image Recognition

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Human-Centered AI for Medical Imaging

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XAI-based Data Visualization in Multimodal Medical DataPreprint

Sharma, S., Singh, M., Mcdaid, L. J., & Bhattacharyya, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.11.664302 · Cited by 6 · see citing works

An Explainable AI Application (AF’fective) to Support Monitoring of Patients With Atrial Fibrillation After Catheter Ablation: Qualitative Focus Group, Design Session, and Interview Study

She, W. J., Siriaraya, P., Iwakoshi, H., Kuwahara, N., & Senoo, K. (2025) · JMIR Human Factors · doi:10.2196/65923 · Cited by 5 · see citing works

Which explainable AI methods in medical imaging are clinically impactful? A systematic literature review addressing the clinician's perspective

Din, S. U., Kemna, R., Ket, J. C. F., Iqbal, M., Bohoudi, O., Hoogendoorn, M., Beretta, E., & Lisowska, A. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1819422 · Cited by 7 · see citing works

Human-centered AI as a framework guiding the development of image-based diagnostic tools in oncology: a systematic review

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Explainable AI for medical image analysis

Brás, C., Montenegro, H., Cai, L. Y., Corbetta, V., Huo, Y., Silva, W., Cardoso, J. S., Landman, B. A., & Išgum, I. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-323761-4.00028-6 · Cited by 5 · see citing works

Developing and assessing an AI-based multi-task prediction system to assist radiologists detecting lung diseases in reading chest x-ray images

Guo, L., Hong, K., Xiao, Q., Qian, L., Jaeger, S., Zheng, B., Quan, S., Li, X., Cheng, G., & Lure, Y. M. F. (2023) · SPIE Medical Imaging: Image Perception, Observer Performance, and Technology Assessment · doi:10.1117/12.2652338 · Cited by 5 · see citing works

AF’fective Design: Supporting Atrial Fibrillation Post-treatment with Explainable AI

She, W. J., Senoo, K., Iwakoshi, H., Kuwahara, N., & Siriaraya, P. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490100.3516455 · Cited by 5 · see citing works

When Traditional Medicine Meets AI: Critical Considerations for AI-Empowered Clinical Support in Traditional Medicine

Sun, Y., Yue, W., Jin, X., Ma, S., Ma, X., & Wang, X. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757705 · Cited by 4 · see citing works

The Role of Explainable AI (XAI) in Transforming Healthcare Decision-Making

Barma, N. D. (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.14677038

A User-Centric Analysis of Explainability in AI-Based Medical Image DiagnosisPreprint

Wagner, J., & Schlippe, T. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2605.02903

Outlining the Design Space of Explainable Intelligent Systems for Medical DiagnosisPreprint

Xie, Y., Gao, G., & Chen, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.06019 · Cited by 47 · see citing works

Explainable AI in Healthcare Application

Sindiramutty, S. R., Tee, W. J., Balakrishnan, S., Kaur, S., Thangaveloo, R., Jazri, H., Khan, N. A., Gharib, A. H., & Manchuri, A. R. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-6684-6361-1.ch005 · Cited by 38 · see citing works

Intelligent systems in healthcare: A systematic survey of explainable user interfaces

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