Mike Schaekermann
Mike Schaekermann is a Research Scientist at Google where he co-leads Project AMIE, Google's flagship research project for AI in clinical reasoning and conversations. His research is located at the intersection of human-computer interaction, artificial intelligence and medicine. Prior to joining Google, Mike received a PhD in Computer Science from the University of Waterloo, Canada, and an undergraduate engineering degree from Salzburg University of Applied Sciences, Austria, and completed his State Exam I in Medicine at the University of Marburg, Germany. His research has been published in top-tier venues like Nature, Nature Medicine, NEJM AI and CHI, and has been recognized by multiple awards including the Google PhD Fellowship and the Canadian Computer Science Distinguished Dissertation Award.
Authored Publications
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Towards conversational artificial intelligence for disease management
Khaled Saab
David Stutz
Yong Cheng
Kavita Kulkarni
Sara Mahdavi
Joelle Barral
James Manyika
Ryutaro Tanno
Adam Rodman
Nature (2026)
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Although large language models have shown promise in diagnostic dialogue, their capabilities for effective management reasoning, including disease progression, therapeutic response and safe medication prescription, have remained underexplored. We have advanced the previously demonstrated diagnostic capabilities of the Articulate Medical Intelligence Explorer (AMIE) using a new large-language-model-based agentic system optimized for multivisit clinical management and dialogue. To ground the reasoning of AMIE in authoritative clinical knowledge, we leveraged the long-context capabilities of Gemini, combining in-context retrieval with structured reasoning to align its output with up-to-date clinical practice guidelines and drug formularies. In a randomized, blinded virtual Objective Structured Clinical Examination study, AMIE was compared to 21 primary care physicians (PCPs) across 100 multivisit case scenarios designed to reflect the guidance of the UK National Institute for Health and Care Excellence and BMJ Best Practice guidelines. AMIE was non-inferior to PCPs in management reasoning, as assessed by specialists, and scored better both with respect to preciseness of treatment and investigation, and in terms of its alignment with and grounding in clinical guidelines. To benchmark medication reasoning, we developed RxQA, a multiple-choice question benchmark that was derived from two national drug formularies (from the USA and UK) and validated by board-certified pharmacists. Although AMIE and PCPs both benefited from the ability to access external drug information, AMIE outperformed PCPs on higher-difficulty questions. Although further research will be needed before real-world translation of AMIE, its strong performance across evaluations marks a significant step towards use of conversational artificial intelligence as a tool in disease management.
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A large language model for complex cardiology care
Jack W O’Sullivan
Khaled Saab
Daniel K. Amponsah
Evaline Cheng
Yong Cheng
Emily Chu
Yaanik Desai
Aly Elezaby
Muhammad Fazal
Tasmeen Hussain
Sneha S. Jain
Daniel Seung Kim
Roy Lan
Jiwen Li
Wilson Tang
Natalie Tapaskar
Victoria Parikh
Ryan Sandoval
Gabriela Spencer-Bonilla
Bryan Wu
Kavita Kulkarni
Philip Mansfield
Juro Gottweis
Joelle Barral
Ryutaro Tanno
Sara Mahdavi
Euan Ashley
Nature Medicine (2026)
Preview abstract
The scarcity of subspecialist medical expertise poses a considerable challenge for healthcare delivery. This issue is particularly acute in cardiology, where timely, accurate management determines outcomes. We explored the potential of Articulate Medical Intelligence Explorer (AMIE), a large language model-based experimental medical artificial intelligence system, to augment clinical decision-making in this challenging context. We conducted a randomized controlled trial comparing large language model-assisted care with the usual care of complex patients suspected of having a genetic cardiomyopathy, and we curated a real-world dataset of complex cases from a subspecialist cardiology practice. Nine participating general cardiologists were provided with access to both clinical text reports and raw diagnostic data—including electrocardiograms, echocardiograms, cardiac magnetic resonance imaging scans and cardiopulmonary exercise testing—and were randomized to manage these cases, either with or without assistance from AMIE. We developed a ten-domain evaluation rubric used by three blinded subspecialists to evaluate the quality of triage, diagnosis and management. In our randomized controlled trial with retrospective patient data, subspecialists favored large language model-assisted responses overall, and for the management plan and diagnostic testing domains, with the remaining domains considered a tie. Overall, subspecialists preferred AMIE-assisted cardiology assessments 46.7% of the time, compared with 32.7% for cardiologists alone (P = 0.02), with 20.6% rated as a tie. Subspecialists also quantified errors, extra and missing content, reasoning and potential bias. Cardiologists alone had more clinically significant errors (24.3% versus 13.1%, P = 0.033) and more missing content (37.4% versus 17.8%, P = 0.0021) than cardiologists assisted by AMIE. Lastly, cardiologists who used AMIE reported that AMIE helped their assessment more than half the time (57.0%) and saved time in 50.5% of cases.
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SymptomAI: Toward a Conversational AI Agent for Everyday Symptom Assessment
Joe Breda
Fadi Yousif
Beszel Hawkins
Marinela Cotoi
Miao Liu
Ray Luo
Sam Schmidgall
Girish Narayanswamy
Samuel Solomon
Max Xu
Longfei Shangguan
Bhavna Daryani
Buddy Herkenham
Cara Tan
Mark Malhotra
Shwetak Patel
Zach Wasson
Dimitrios Antos
Bob Lou
Matthew Thompson
Jonathan Richina
Anupam Pathak
Nichole Young-Lin
Jake Sunshine
Daniel McDuff
Arxiv preprint, 2605.040 (2026) (to appear)
Preview abstract
Language models excel at diagnostic assessments on curated medical case-studies and vignettes, performing on par with, or better than, clinical professionals. However, existing studies focus on complex scenarios with rich context making it difficult to draw conclusions about how these systems perform for patients reporting symptoms in everyday life. We deployed SymptomAI, a set of conversational AI agents for end-to-end patient interviewing and differential diagnosis (DDx), via the Fitbit app in a study that randomized participants (N=13,917) to interact with five AI agents. This corpus captures diverse communication and a realistic distribution of illnesses from a real world population. A subset of 1,228 participants reported a clinician-provided diagnosis, and 517 of these were further evaluated by a panel of clinicians during over 250 hours of annotation. SymptomAI DDx were significantly more accurate (OR = 2.56, p < 0.001) than those from independent clinicians given the same dialogue in a blinded randomized comparison. Moreover, agentic strategies which conduct a dedicated symptom interview that elicit additional symptom information before providing a diagnosis, perform substantially better than baseline, user-guided conversations (p < 0.001). An auxiliary analysis on 1,509 conversations from a general US population panel validated that these results generalize beyond wearable device users. We used SymptomAI diagnoses as labels for all 13,917 participants to analyze over 500,000 days of wearable metrics across nearly 400 unique conditions. We identified strong associations between acute infections and physiological shifts (e.g., OR > 7 for influenza). While limited by self-reported ground truth, these results demonstrate the benefits of a dedicated and complete symptom interview compared to a user-guided symptom discussion, which is the default of most consumer LLMs.
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Prospective evidence for conversational medical AI is hard, but non-negotiable
Adam Rodman
Ami Parekh
Ethan Goh
Christina Chen
Joelle Barral
Nature Medicine (2026)
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Trust in clinical artificial intelligence (AI) cannot be benchmarked into existence. It must be earned through rigorous prospective studies in real-world clinical settings, where the hardest lessons often concern the humans and systems around the AI, not the technology itself.
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Conversational diagnostic artificial intelligence in ambulatory primary care: a prospective feasibility study
Peter Brodeur
Jacob M. Koshy
Khaled Saab
Ava Homiar
Roma Ruparel
Charles Wu
Ryutaro Tanno
Joseph Xu
Amy Wang
David Stutz
Hannah M. Ferrera
David Barrett
Lindsey Crowley
Jihyeon Lee
Spencer E. Rittner
Selena K. Zhang
Elahe Vedadi
Christine G. Kohn
Kavita Kulkarni
Vinay Kadiyala
Sara Mahdavi
Wendy Du
David Feinbloom
Renee Wong
Petar Sirkovic
Alessio Orlandi
Juro Gottweis
Joelle Barral
Kat Chou
James Manyika
Rob Fields
Jonathan X. Li
Marc L. Cohen
Adam Rodman
The Lancet (2026)
Preview abstract
Background: Artificial intelligence (AI)-based systems show promise for assisting primary care providers (PCPs) with patient care. We aimed to evaluate the safety and quality of clinical conversations of a patient-facing conversational AI system, which engaged in real-world urgent primary care appointments.
Methods: In this prospective, single-centre, single-arm feasibility study, English-speaking patients aged at least 18 years interacted with the Articulate Medical Intelligence Explorer (AMIE) up to 5 days before a single-complaint urgent primary care appointment. Physician safety supervisors monitored all interactions and were trained to intervene on the basis of predefined safety criteria. AMIE transcripts and summaries were shared with PCPs before the visit. Primary outcomes were the number of supervised conversation safety stops, AMIE’s conversation quality assessed by clinical evaluators, and patient and PCP experiences per surveys. This study is registered with ClinicalTrials.gov (NCT06911398).
Findings: From April to November, 2025, 114 patients were enrolled with 98 completing both the AMIE interaction and the PCP appointment. Zero conversation safety stops were required on the basis of prespecified criteria. Safety supervisors noted one hallucination and added clinical information in five interactions. AMIE’s conversations were rated favourably in 87–100% of cases (17 criteria) by clinical evaluators, and 48–96% (16 criteria) by patients. Patient attitudes towards AI improved after interacting with AMIE and remained elevated after the patient’s visit with their physician. PCPs completed post-surveys in 60 of 98 cases, including 44 cases in which they reviewed the AMIE transcript before the visit. PCPs found AMIE helpful for visit preparation in 33 of 44 cases and reported that it might have changed their behaviour in 25 of 44 cases.
Interpretation: Although further research is needed, this study shows the initial feasibility of conversational AI in a real-world setting—assessed via conversation safety and quality, as well as user acceptance—and represents a crucial step towards clinical translation.
Funding: Alphabet.
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Towards expert-level medical AI for real-time video consultations
Mahvish Nagda
Jihyeon Lee
Matthew Thompson
CJ Park
Tim Strother
Roma Ruparel
Teya Bergamaschi
Suhana Bedi
Meet Shah
Pavel Dubov
Toshiyuki Fukuzawa
Sam Schmidgall
Craig Schiff
Joseph Xu
Aliya Rysbek
Yana Lunts
Jan Freyberg
Rebecca Hemenway
David Racz
Carey Radebaugh
Joelle Barral
Kavi Goel
Kat Chou
James Manyika
Gregory Wayne
Yun Liu
Ethan Goh
Christina Chen
Ryutaro Tanno
arXiv (2026)
Preview abstract
Audio-visual interaction is the standard for patient-physician consultations, enabling natural communication and effective assessment of illness through non-verbal cues. While text-based AI has shown promise, it discards essential perceptual dimensions and limits patients who cannot articulate symptoms in writing. Early efforts to extend medical AI to audio-visual interaction have demonstrated feasibility, but not reached clinician-level performance. Here, we provide the first demonstration of expert-level AI in real-time clinical video consultations using AMIE (Articulate Medical Intelligence Explorer) in a video configuration. AMIE (Video) is a Gemini-based multi-agent system integrating low-latency dialogue, clinical reasoning, and real-time audio-visual perception. To guide development, we established a taxonomy and automated evaluations for clinical audio-visual cues in telehealth settings. In a randomized Objective Structured Clinical Examination (OSCE) study with 30 primary care physicians (PCPs), 15 patient actors and 100 clinical scenarios, we compared AMIE (Video), its text-only counterpart AMIE (Text), and PCPs consulting via video. Clinical evaluators rated AMIE (Video) on par or better than PCPs in history-taking, diagnosis, management, and physical observation and examination. Patient actors preferred AMIE's approach to assessing and explaining conditions, while PCPs were preferred for rapport and partnership building. In modality ablation, patient actors preferred AMIE (Video)'s interface over text chat for communicative effectiveness, convenience, and feeling understood. Limitations remain in fine anatomical precision, subtle affective nuances, and high-frequency movements. While further research is needed before real-world translation, these results mark an important milestone toward AI systems capable of augmenting care across the sensory complexity of clinical practice.
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Toward a test of medical AI superintelligence
Ethan Goh
David Wu
Chase Walton
Liam McCoy
Anastasia Perez
Laura Wegner
Fateme Nateghi Haredasht
Luyang Luo
Kathleen Lacar
Thomas Buckley
Austin Schoeffler
Peter Brodeur
Kameron C. Black
John Havlik
John Rumsfeld
Daniel Lopez-martinez
Paxton Maeder-York
Karan Singhal
David Gunning
Bon Ku
Haider Warraich
Shantanu Nundy
Vishnu Ravi
Arnold Milstein
Jason Hom
Kevin Schulman
Pranav Rajpurkar
Arjun Manrai
Robert Wachter, MD
Eric Topol
Eric horvitz
Adam Rodman
Jonathan Chen
Nature Medicine (2026)
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Researchers urgently need a rigorous, task-based framework to define and measure medical AI ‘superintelligence’, because existing benchmarks are misleading and insufficient.
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The emergence of Generative AI (GenAI) presents both vast opportunities and profound challenges for medical education. To ensure its effective and ethically sound adoption, educators, institutions, and policy-makers must look beyond efficiency gains to foster deeper, resilient learning catalyzed by productive struggle: deliberate engagement with complex issues that promotes critical thinking. We conducted a conceptual analysis exploring two intertwined dimensions: using GenAI as a pedagogical tool (‘AI in Education’) and teaching foundational understanding of AI itself (‘Education in AI’). We mapped GenAI’s affordances—expressivity, interactivity, multimodality—to educational experiences such as interactive tutoring, OSCE feedback, and simulated patient interactions. We then critically examined implications for curriculum design and learner competencies. We found that GenAI’s simultaneous increase in helpfulness and complexity forces convergence of the two educational dimensions. While GenAI supports enhanced learning (e.g. multi-turn tutoring), meaningful use demands critical awareness of its operational limits, ethical boundaries, and technical mechanisms. We argue that mere use of GenAI is insufficient; effective engagement must be informed, reflective, and critical. This entanglement necessitates reimagined curricula and competencies to prepare adaptive, ethically grounded clinicians. Embracing the tension between using and understanding GenAI supports development of robust clinical reasoning in an AI-augmented healthcare landscape.
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Exploring large language models for specialist-level oncology care
Vikram Dhillon
Polly Niravath
Preethi Prasad
Khaled Saab
Ryutaro Tanno
Yong Cheng
Hanh Mai
Ethan Burns
Zainub Ajmal
Kavita Kulkarni
Philip Mansfield
Joelle Barral
Juro Gottweis
Sara Mahdavi
NEJM AI (2025)
Preview abstract
Large language models have shown rapid progress in encoding clinical knowledge and demonstrating clinical reasoning. However, their capabilities in subspecialty or complex medical settings remain underexplored. In this work, we probe the performance of Articulate Medical Intelligence Explorer (AMIE), a conversational diagnostic AI system in the subspecialty of breast oncology care without specific fine-tuning to this challenging domain. To perform this evaluation, we curated a set of 60 synthetic breast cancer vignettes representing a range of treatment-naive, treatment-refractory, and rare histology cases encountered in a community-based breast oncology clinic. We developed a detailed clinical rubric for evaluating management plans, including axes such as the quality of case summarization, safety of the proposed care plan, and recommendations for treatment (i.e., chemotherapy, radiotherapy, surgery, and hormonal therapy). To improve performance, we enhanced AMIE with the inference-time ability to perform web search retrieval to gather relevant and up-to-date clinical knowledge and refine its responses with a multistage, self-critique pipeline. We compare the response quality of AMIE with that of internal medicine trainees, oncology fellows, and general oncology attendings under both automated and specialist clinician evaluations. Although our evaluations were limited to a few physicians, AMIE outperformed trainees and fellows, demonstrating the potential of the system in this important domain. However, AMIE’s performance was overall inferior to that of attending oncologists, suggesting that further prospective research is needed.
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Toward expert-level medical question answering with large language models
Karan Singhal
Juro Gottweis
Le Hou
Kevin Clark
Heather Cole-Lewis
Amy Wang
Sami Lachgar
Philip Mansfield
Sushant Prakash
Bradley Green
Ewa Dominowska
Nenad Tomašev
Renee Wong
Sara Mahdavi
Joelle Barral
Nature Medicine (2025)
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Large language models (LLMs) have shown promise in medical question answering, with Med-PaLM being the first to exceed a ‘passing’ score in United States Medical Licensing Examination style questions. However, challenges remain in long-form medical question answering and handling real-world workflows. Here, we present Med-PaLM 2, which bridges these gaps with a combination of base LLM improvements, medical domain fine-tuning and new strategies for improving reasoning and grounding through ensemble refinement and chain of retrieval. Med-PaLM 2 scores up to 86.5% on the MedQA dataset, improving upon Med-PaLM by over 19%, and demonstrates dramatic performance increases across MedMCQA, PubMedQA and MMLU clinical topics datasets. Our detailed human evaluations framework shows that physicians prefer Med-PaLM 2 answers to those from other physicians on eight of nine clinical axes. Med-PaLM 2 also demonstrates significant improvements over its predecessor across all evaluation metrics, particularly on new adversarial datasets designed to probe LLM limitations (P < 0.001). In a pilot study using real-world medical questions, specialists preferred Med-PaLM 2 answers to generalist physician answers 65% of the time. While specialist answers were still preferred overall, both specialists and generalists rated Med-PaLM 2 to be as safe as physician answers, demonstrating its growing potential in real-world medical applications.
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