Designing for Curiosity: An Interdisciplinary Workshop. - Edith Law; Pierre-Yves Oudeyer; Ming Yin; Mike Schaekermann; Alex C. Williams (CHI Extended Abstracts, 2017)

Testing Incremental Difficulty Design in Platformer Games. - Rina R. Wehbe; Elisa D. Mekler; Mike Schaekermann; Edward Lank; Lennart E. Nacke (CHI, 2017)

Curiously Motivated: Profiling Curiosity with Self-Reports and Behaviour Metrics in the Game "Destiny". - Mike Schaekermann; Giovanni Ribeiro; Guenter Wallner; Simone Kriglstein; Daniel M. Johnson 001; Anders Drachen; Rafet Sifa; Lennart E. Nacke (CHI PLAY, 2017)

Online Bayesian Transfer Learning for Sequential Data Modeling. - Priyank Jaini; Zhitang Chen; Pablo Carbajal; Edith Law; Laura Middleton; Kayla Regan; Mike Schaekermann; George Trimponias; James Tung; Pascal Poupart (ICLR (Poster), 2017)

Expert Disagreement in Sequential Labeling: A Case Study on Adjudication in Medical Time Series Analysis. - Mike Schaekermann; Edith Law; Kate Larson; Andrew Lim 002 (SAD/CrowdBias@HCOMP, 2018)

Deep Learning to Assess Glaucoma Risk and Associated Features in Fundus Images. - Sonia Phene; R. Carter Dunn; Naama Hammel; Yun Liu 013; Jonathan Krause; Naho Kitade; Mike Schaekermann; Rory Sayres; Derek J. Wu; Ashish Bora; Christopher Semturs; Anita Misra; et al. (CoRR, 2018)

Resolvable vs. Irresolvable Disagreement: A Study on Worker Deliberation in Crowd Work. - Mike Schaekermann; Joslin Goh; Kate Larson; Edith Law (Proc. ACM Hum. Comput. Interact., 2018)

Trusted AI and the Contribution of Trust Modeling in Multiagent Systems. - Robin Cohen; Mike Schaekermann; Sihao Liu; Michael Cormier (AAMAS, 2019)

Capturing Expert Arguments from Medical Adjudication Discussions in a Machine-readable Format. - Mike Schaekermann; Graeme Beaton; Minahz Habib; Andrew Lim 002; Kate Larson; Edith Law (WWW (Companion Volume), 2019)

Understanding Expert Disagreement in Medical Data Analysis through Structured Adjudication. - Mike Schaekermann; Graeme Beaton; Minahz Habib; Andrew Lim 002; Kate Larson; Edith Law (Proc. ACM Hum. Comput. Interact., 2019)

Ambiguity-aware AI Assistants for Medical Data Analysis. - Mike Schaekermann; Graeme Beaton; Elaheh Sanoubari; Andrew Lim 002; Kate Larson; Edith Law (CHI, 2020)

Expert Discussions Improve Comprehension of Difficult Cases in Medical Image Assessment. - Mike Schaekermann; Carrie J. Cai; Abigail E. Huang; Rory Sayres (CHI, 2020)

The AI Bookie - Place Your Bets: Will Machine Learning Outgrow Human Labeling? - Mike Schaekermann; Christopher Homan; Lora Aroyo; Praveen K. Paritosh; Kurt D. Bollacker; Chris Welty (AI Mag., 2020)

Human-AI Interaction in the Presence of Ambiguity: From Deliberation-based Labeling to Ambiguity-aware AI. - Mike Schaekermann (2020)

Investigating and Mitigating Biases in Crowdsourced Data. - Danula Hettiachchi; Mark Sanderson; Jorge Gonçalves 001; Simo Hosio; Gabriella Kazai; Matthew Lease; Mike Schaekermann; Emine Yilmaz (CSCW Companion, 2021)

Proceedings of the CSCW 2021 Workshop - Investigating and Mitigating Biases in Crowdsourced Data. - Danula Hettiachchi; Mark Sanderson; Jorge Gonçalves 001; Simo Hosio; Gabriella Kazai; Matthew Lease; Mike Schaekermann; Emine Yilmaz (CoRR, 2021)

The Challenge of Variable Effort Crowdsourcing and How Visible Gold Can Help. - Danula Hettiachchi; Mike Schaekermann; Tristan McKinney; Matthew Lease (Proc. ACM Hum. Comput. Interact., 2021)

In Search of Ambiguity: A Three-Stage Workflow Design to Clarify Annotation Guidelines for Crowd Workers. - Vivek Krishna Pradhan; Mike Schaekermann; Matthew Lease (Frontiers Artif. Intell., 2022)

Data excellence for AI: why should you care? - Lora Aroyo; Matthew Lease; Praveen K. Paritosh; Mike Schaekermann (Interactions, 2022)

Towards Expert-Level Medical Question Answering with Large Language Models. - Karan Singhal; Tao Tu 001; Juraj Gottweis; Rory Sayres; Ellery Wulczyn; Le Hou; Kevin Clark; Stephen Pfohl; Heather Cole-Lewis; Darlene Neal; Mike Schaekermann; Amy Wang; et al. (CoRR, 2023)

Evaluating AI systems under uncertain ground truth: a case study in dermatology. - David Stutz; Ali Taylan Cemgil; Abhijit Guha Roy; Tatiana Matejovicova; Melih Barsbey; Patricia Strachan; Mike Schaekermann; Jan Freyberg; Rajeev V. Rikhye; Beverly Freeman; Javier Perez Matos; Umesh Telang; et al. (CoRR, 2023)

Towards Generalist Biomedical AI. - Tao Tu 001; Shekoofeh Azizi; Danny Driess; Mike Schaekermann; Mohamed Amin; Pi-Chuan Chang; Andrew Carroll; Chuck Lau; Ryutaro Tanno; Ira Ktena; Basil Mustafa; Aakanksha Chowdhery; et al. (CoRR, 2023)

Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation. - Ryutaro Tanno; David G. T. Barrett; Andrew Sellergren; Sumedh Ghaisas; Sumanth Dathathri; Abigail See; Johannes Welbl; Karan Singhal; Shekoofeh Azizi; Tao Tu 001; Mike Schaekermann; Rhys May; et al. (CoRR, 2023)

Towards Accurate Differential Diagnosis with Large Language Models. - Daniel McDuff; Mike Schaekermann; Tao Tu 001; Anil Palepu; Amy Wang; Jake Garrison; Karan Singhal; Yash Sharma; Shekoofeh Azizi; Kavita Kulkarni; Le Hou; Yong Cheng; et al. (CoRR, 2023)

Conversational AI in health: Design considerations from a Wizard-of-Oz dermatology case study with users, clinicians and a medical LLM. - Brenna Li; Amy Wang; Patricia Strachan; Julie Anne Séguin; Sami Lachgar; Karyn C. Schroeder; Mathias S. Fleck; Renee Wong; Alan Karthikesalingam; Vivek Natarajan; Yossi Matias; Gregory S. Corrado; et al. (CHI Extended Abstracts, 2024)

Towards Conversational Diagnostic AI. - Tao Tu 001; Anil Palepu; Mike Schaekermann; Khaled Saab 003; Jan Freyberg; Ryutaro Tanno; Amy Wang; Brenna Li; Mohamed Amin; Nenad Tomasev; Shekoofeh Azizi; Karan Singhal; et al. (CoRR, 2024)

MINT: A wrapper to make multi-modal and multi-image AI models interactive. - Jan Freyberg; Abhijit Guha Roy; Terry Spitz; Beverly Freeman; Mike Schaekermann; Patricia Strachan; Eva Schnider; Renee Wong; Dale R. Webster; Alan Karthikesalingam; Yun Liu 013; Krishnamurthy Dvijotham; et al. (CoRR, 2024)

A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models. - Stephen R. Pfohl; Heather Cole-Lewis; Rory Sayres; Darlene Neal; Mercy Nyamewaa Asiedu; Awa Dieng; Nenad Tomasev; Qazi Mamunur Rashid; Shekoofeh Azizi; Negar Rostamzadeh; Liam G. McCoy; Leo Anthony Celi; et al. (CoRR, 2024)

Capabilities of Gemini Models in Medicine. - Khaled Saab 003; Tao Tu 001; Wei-Hung Weng; Ryutaro Tanno; David Stutz; Ellery Wulczyn; Fan Zhang; Tim Strother; Chunjong Park; Elahe Vedadi; Juanma Zambrano Chaves; Szu-Yeu Hu; et al. (CoRR, 2024)

Towards Democratization of Subspeciality Medical Expertise. - Jack W. O'Sullivan; Anil Palepu; Khaled Saab 003; Wei-Hung Weng; Yong Cheng; Emily Chu; Yaanik Desai; Aly Elezaby; Daniel Seung Kim; Roy Lan; Wilson Tang; Natalie Tapaskar; et al. (CoRR, 2024)

Exploring Large Language Models for Specialist-level Oncology Care. - Anil Palepu; Vikram Dhillon; Polly Niravath; Wei-Hung Weng; Preethi Prasad; Khaled Saab 003; Ryutaro Tanno; Yong Cheng; Hanh Mai; Ethan Burns; Zainub Ajmal; Kavita Kulkarni; et al. (CoRR, 2024)

LearnLM: Improving Gemini for Learning. - Abhinit Modi; Aditya Srikanth Veerubhotla; Aliya Rysbek; Andrea Huber; Brett Wiltshire; Brian Veprek; Daniel Gillick; Daniel Kasenberg; Derek Ahmed; Irina Jurenka; James Cohan; Jennifer She; et al. (CoRR, 2024)

Navigating Skin Concerns with AI: A Human-Centered Investigation of a Dermatology App in a Diverse Community. - Rory Sayres; Anna Devon-Sand; Mike Schaekermann; Margaret Ann Smith; Patricia Strachan; Grace Eunhae Hong; Trinh Nguyen; Doris Wong; Naama Hammel; Dawn Siegel; Kexin Ren; Chelsea Belletieri; et al. (CHI Extended Abstracts, 2025)

Generative AI for medical education: Insights from a case study with medical students and an AI tutor for clinical reasoning. - Amy Wang; Roma Ruparel; Anna Iurchenko; Paul Jhun; Julie Anne Séguin; Patricia Strachan; Renee Wong; Alan Karthikesalingam; Yossi Matias; Avinatan Hassidim; Dale R. Webster; Christopher Semturs; et al. (CHI Extended Abstracts, 2025)

Evaluating medical AI systems in dermatology under uncertain ground truth. - David Stutz; Ali Taylan Cemgil; Abhijit Guha Roy; Tatiana Matejovicova; Melih Barsbey; Patricia Strachan; Mike Schaekermann; Jan Freyberg; Rajeev Rikhye; Beverly Freeman; Javier Perez Matos; Umesh Telang; et al. (Medical Image Anal., 2025)

Towards Conversational AI for Disease Management. - Anil Palepu; Valentin Liévin; Wei-Hung Weng; Khaled Saab 003; David Stutz; Yong Cheng; Kavita Kulkarni; S. Sara Mahdavi; Joelle K. Barral; Dale R. Webster; Katherine Chou; Avinatan Hassidim; et al. (CoRR, 2025)

Advancing Conversational Diagnostic AI with Multimodal Reasoning. - Khaled Saab 003; Jan Freyberg; Chunjong Park; Tim Strother; Yong Cheng; Wei-Hung Weng; David G. T. Barrett; David Stutz; Nenad Tomasev; Anil Palepu; Valentin Liévin; Yash Sharma; et al. (CoRR, 2025)

MedPAIR: Measuring Physicians and AI Relevance Alignment in Medical Question Answering. - Yuexing Hao; Kumail Alhamoud; Hyewon Jeong; Haoran Zhang 003; Isha Puri; Philip Torr 001; Mike Schaekermann; Ariel D. Stern; Marzyeh Ghassemi (CoRR, 2025)

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities. - Gheorghe Comanici; Eric Bieber; Mike Schaekermann; Ice Pasupat; Noveen Sachdeva; Inderjit S. Dhillon; Marcel Blistein; Ori Ram; Dan Zhang; Evan Rosen; Luke Marris; Sam Petulla; et al. (CoRR, 2025)

Towards physician-centered oversight of conversational diagnostic AI. - Elahe Vedadi; David G. T. Barrett; Natalie Harris; Ellery Wulczyn; Shashir Reddy; Roma Ruparel; Mike Schaekermann; Tim Strother; Ryutaro Tanno; Yash Sharma; Jihyeon Lee; Cían Hughes; et al. (CoRR, 2025)