The Collective Surgical Consciousness: Artificial Intelligence & the Future of Surgery
Guest speaker Doctor Ozanan Meireles, the Director of the Surgical AI and Innovation Lab at Massachusetts General Hospital and a faculty member at Harvard Medical School, presents The Collective Surgical Consciousness: Artificial Intelligence & the Future of Surgery.
Objectives:
* Become familiar with the subfields of AI used in surgery
* Understand the importance of a potential paradigm shift in surgical practice, training, and continue medical development
* The importance of data acquisition, sharing and ownership, and development of machine learning algorithms
Presented by the Stony Brook University Hospital Institutional Ethics Committee and co-sponsored by the Center for Medical Humanities, Compassionate Care and Bioethics, Stony Brook University
9th Annual Medical Ethics Symposium
Friday, August 7, 2026: MART Auditorium live and via Webinar
Artificial intelligence (AI) is transforming healthcare by improving diagnosis, streamlining administrative tasks, supporting personalized treatment plans, and aiding medical research. However, the growing use of AI in medicine also raises important ethical concerns. While AI has the potential to enhance patient care and efficiency, healthcare providers and patients must approach these technologies with caution and critical oversight.
This event invites professionals and students from all healthcare, legal, and other associated disciplines to bring their work, challenges, and solutions to the table at 'Trust but Verify: Ethical Challenges of AI in Healthcare.'
Agenda:
8am Registration/Light Breakfast
Provided by The Center for Medical Humanities, Compassionate Care and Bioethics
8:30am Welcome
Jean Mueller, MPS, BS, RN, CPHQ Ethics Symposium Coordinator
Opening Remarks
Carolyn Santora, MS, RN, NEA-BC, CPHQ, Chief Nursing Officer; Chief Nursing Officer; Chief of Regulatory Affairs, Patient Safety and Ethics, Stony Brook University Hospital; Chair/Institutional Ethics Committee, Stony Brook Medicine
KEYNOTE
9- 10:30am Trust, Communication, and Consent in AI-Mediated Healthcare
Kellie Owens, PhD
Assistant Professor, Medical Ethics, Department of Population Health, NYU Grossman School of Medicine
KEYNOTE
10:45- 12:15pm AI That Measures, AI that Speaks: A Decade of Surgical AI and the Problem of Verification
Alexander Winkler- Schwartz, MDCM, PhD, FRCSC, FAANS
Neurosurgeon, Neuroscientist, AI and Surgical Education Researcher
Assistant Professor, Adult and Pediatric Neurosurgery, Stony Brook University
12:30- 1:15pm Lunch
Provided by the Stony Brook University Hospital Institutional Ethics Committee
Panel Discussion and Interactive Ethics Case Presentations
1:30- 3pm Healthcare AI: Designed to Assist, Not Resist Human Judgment
Moderator: David N. Hoffman, JD
Distinguished Panelists
Leah Gancz, MD
Julie Luengas, DNP, MBA, RN, NI-BC, FHIMSS
Tauhid Mahmud, MD, MPH
Pons Materum III, MD
Neil J. Patel, MD, MBA, MS
Carolyn Santora, RN, NEA-BC, CPHQ
Caitlyn Tabor, JD, MBE
Mathew Tharakan, MD, MBA
Alexander Winkler-Schwartz, MDCM, PhD, FRCSC, FAANS
Zhi Wu, MD
3- 3:45pm Poster Awards and Rapid-Fire Sessions
Carolyn Santora, MS, RN, NEA-BC, CPHQ
1st, 2nd and 3rd Place Posters and Colleagues Choice Award
3:45- 4pm Summary and Closing Remarks
Carolyn Santora, MS, RN, NEA-BC, CPHQ
Register here.
9th Annual Medical Ethics Symposium
Friday, August 7, 2026: MART Auditorium live and via Webinar
Artificial intelligence (AI) is transforming healthcare by improving diagnosis, streamlining administrative tasks, supporting personalized treatment plans, and aiding medical research. However, the growing use of AI in medicine also raises important ethical concerns. While AI has the potential to enhance patient care and efficiency, healthcare providers and patients must approach these technologies with caution and critical oversight.
This event invites professionals and students from all healthcare, legal, and other associated disciplines to bring their work, challenges, and solutions to the table at 'Trust but Verify: Ethical Challenges of AI in Healthcare.'
Agenda:
8am Registration/Light Breakfast
Provided by The Center for Medical Humanities, Compassionate Care and Bioethics
8:30am Welcome
Jean Mueller, MPS, BS, RN, CPHQ Ethics Symposium Coordinator
Opening Remarks
Carolyn Santora, MS, RN, NEA-BC, CPHQ, Chief Nursing Officer; Chief Nursing Officer; Chief of Regulatory Affairs, Patient Safety and Ethics, Stony Brook University Hospital; Chair/Institutional Ethics Committee, Stony Brook Medicine
KEYNOTE
9- 10:30am Trust, Communication, and Consent in AI-Mediated Healthcare
Kellie Owens, PhD
Assistant Professor, Medical Ethics, Department of Population Health, NYU Grossman School of Medicine
KEYNOTE
10:45- 12:15pm AI That Measures, AI that Speaks: A Decade of Surgical AI and the Problem of Verification
Alexander Winkler- Schwartz, MDCM, PhD, FRCSC, FAANS
Neurosurgeon, Neuroscientist, AI and Surgical Education Researcher
Assistant Professor, Adult and Pediatric Neurosurgery, Stony Brook University
12:30- 1:15pm Lunch
Provided by the Stony Brook University Hospital Institutional Ethics Committee
Panel Discussion and Interactive Ethics Case Presentations
1:30- 3pm Healthcare AI: Designed to Assist, Not Resist Human Judgment
Moderator: David N. Hoffman, JD
Distinguished Panelists
Leah Gancz, MD
Julie Luengas, DNP, MBA, RN, NI-BC, FHIMSS
Tauhid Mahmud, MD, MPH
Pons Materum III, MD
Neil J. Patel, MD, MBA, MS
Carolyn Santora, RN, NEA-BC, CPHQ
Caitlyn Tabor, JD, MBE
Mathew Tharakan, MD, MBA
Alexander Winkler-Schwartz, MDCM, PhD, FRCSC, FAANS
Zhi Wu, MD
3- 3:45pm Poster Awards and Rapid-Fire Sessions
Carolyn Santora, MS, RN, NEA-BC, CPHQ
1st, 2nd and 3rd Place Posters and Colleagues Choice Award
3:45- 4pm Summary and Closing Remarks
Carolyn Santora, MS, RN, NEA-BC, CPHQ
Register here.
2026 MIC Symposium on Multilingualism | Featured Talk
Beyond Cultural Pastiche: Developing CritIC in AI-mediated Clinical Communication
Abstract: Health professional education is taking diversity and cultural sensitivity seriously, and rightly so. As future clinicians work with diverse patients, educators are seeking meaningful, scalable opportunities for communication training. AI-facilitated simulation appears to offer one solution, with avatar patients generated with recognisable cultural cues. Yet this raises an unsettling question: how does AI reproduce culture and what impact does it have on health trainees? Through analysing multimodal clinical communication data from human-AI and human-human interactions, this talk explores fundamental differences in how AI and humans evoke culture. AI-generated cultural talk resembles a pastiche of essentialist cues that flatten context, diminish human criticality, and reproduce structural inequalities. Human cultural talk, by contrast, creates context, expands imagination, and subverts pre-existing boundaries. I conclude by arguing that the next generation of health professionals needs not only EDI awareness but also Critical AI Literacy, conceptualised as CritIC (Critical Interactional Competence), in AI-facilitated clinical communication.
Speaker: David Wei Dai is Associate Professor in Professional Communication at University College London. His research examines social interaction across healthcare education, intercultural and professional communication, language learning and assessment, and human-AI engagement. His work has appeared in leading venues across applied linguistics, health education, and learning analytics, including Applied Linguistics, Language Learning, Annual Review of Applied Linguistics, Medical Education, Advances in Health Sciences Education, BMC Medical Education, and the LAK26 Proceedings. He is the author of the open-access monograph Assessing Interactional Competence. His research has been funded by the British Academy, Leverhulme, the British Council, OET, Duolingo, and TOEFL.
Faculty Response Panel:
Location: Humanities 1006
Beyond Cultural Pastiche: Developing CritIC in AI-mediated Clinical Communication
Abstract: Health professional education is taking diversity and cultural sensitivity seriously, and rightly so. As future clinicians work with diverse patients, educators are seeking meaningful, scalable opportunities for communication training. AI-facilitated simulation appears to offer one solution, with avatar patients generated with recognisable cultural cues. Yet this raises an unsettling question: how does AI reproduce culture and what impact does it have on health trainees? Through analysing multimodal clinical communication data from human-AI and human-human interactions, this talk explores fundamental differences in how AI and humans evoke culture. AI-generated cultural talk resembles a pastiche of essentialist cues that flatten context, diminish human criticality, and reproduce structural inequalities. Human cultural talk, by contrast, creates context, expands imagination, and subverts pre-existing boundaries. I conclude by arguing that the next generation of health professionals needs not only EDI awareness but also Critical AI Literacy, conceptualised as CritIC (Critical Interactional Competence), in AI-facilitated clinical communication.
Speaker: David Wei Dai is Associate Professor in Professional Communication at University College London. His research examines social interaction across healthcare education, intercultural and professional communication, language learning and assessment, and human-AI engagement. His work has appeared in leading venues across applied linguistics, health education, and learning analytics, including Applied Linguistics, Language Learning, Annual Review of Applied Linguistics, Medical Education, Advances in Health Sciences Education, BMC Medical Education, and the LAK26 Proceedings. He is the author of the open-access monograph Assessing Interactional Competence. His research has been funded by the British Academy, Leverhulme, the British Council, OET, Duolingo, and TOEFL.
Faculty Response Panel:
- Yi Wang (Panel Chair; Assistant Professor, Asian and Asian American Studies)
- Renee Fabus (Chair and Program Director, Speech-Language Pathology)
- Andrew Flescher (Professor, English and Program in Public Health)
- Ben Zhang (Assistant Professor, Technology, AI and Society)
Location: Humanities 1006
Abstract: Retrieval-augmented generation (RAG) systems empower large language models (LLMs) to access external knowledge during inference. Recent advances have enabled LLMs to act as search agents via reinforcement learning (RL), improving information acquisition through multi-turn interactions with retrieval engines. However, existing approaches either optimize retrieval using search-only metrics (e.g., NDCG) that ignore downstream utility or fine-tune the entire LLM to jointly reason and retrieve--entangling retrieval with generation and limiting the real search utility and compatibility with frozen or proprietary models. In this work, we propose s3, a lightweight, model-agnostic framework that decouples the searcher from the generator and trains the searcher using a Gain Beyond RAG reward: the improvement in generation accuracy over naïve RAG. s3 requires only 2.4k training samples to outperform baselines trained on over 70 × more data, consistently delivering stronger downstream performance across six general QA and five medical QA benchmarks.
Speaker: Peter Zeng
Location: CS2311
Speaker: Peter Zeng
Location: CS2311
Looking to learn about a new topic or skill? Look no further! Gemini's Guided Learning feature acts as your own personal tutor, teaching you about a particular subject through an engaging back and forth conversation. This AI tool helps users develop their knowledge and skills on a wide variety of topics, acting as a patient mentor, breaking down complex topics step-by-step. This session will take place on 2/24 at 11 AM. Please register using the link below!
https://stonybrookuniversity.co1.qualtrics.com/jfe/form/SV_a9PVlBw0E1Bal1A?
https://stonybrookuniversity.co1.qualtrics.com/jfe/form/SV_a9PVlBw0E1Bal1A?
The Tiger Team open house will be Monday, September 20 at high noon by Zoom:
https://stonybrook.zoom.us/j/
The International Neuroethics Society (INS) Speaker Series on AI & Consciousness
Abstract: Colln Allen and I noted in our 2008 book Moral Machines: Teaching Robots Right From Wrong, that consciousness, a theory of mind, sociability, situational awareness and embodiment are all supra-rational (beyond reason) capabilities that contribute to making ethical decision Whether any of these can be fully instantiated in machines remains an open question. Nevertheless, moral decision making in the digital age will require an evolution in and refinement of specific skills for both humans and for AI. I call one of these evolutions in moral decision making capabilities tradeoff ethics and another a silent ethics. Aspects of this social, and not just technological evolution, will require research by neuroscientists.
Speaker Bio: Wendell Wallach has an international reputation as an expert on the ethics and governance of emerging technologies, particularly artificial intelligence and biotechnologies. He is also senior advisor to The Hastings Center and a scholar at the Yale University Interdisciplinary Center for Bioethics where he chaired Technology and Ethics studies for eleven years. Wallach's latest book, a primer on emerging technologies, is entitled, A Dangerous Master: How to keep technology from slipping beyond our control. He co-authored (with Colin Allen) Moral Machines: Teaching Robots Right From Wrong. Wallach has been referred to as, a Godfather of AI Ethics.
Pre-register here (required): https://umaryland.zoom.us/
Abstract: Colln Allen and I noted in our 2008 book Moral Machines: Teaching Robots Right From Wrong, that consciousness, a theory of mind, sociability, situational awareness and embodiment are all supra-rational (beyond reason) capabilities that contribute to making ethical decision Whether any of these can be fully instantiated in machines remains an open question. Nevertheless, moral decision making in the digital age will require an evolution in and refinement of specific skills for both humans and for AI. I call one of these evolutions in moral decision making capabilities tradeoff ethics and another a silent ethics. Aspects of this social, and not just technological evolution, will require research by neuroscientists.
Speaker Bio: Wendell Wallach has an international reputation as an expert on the ethics and governance of emerging technologies, particularly artificial intelligence and biotechnologies. He is also senior advisor to The Hastings Center and a scholar at the Yale University Interdisciplinary Center for Bioethics where he chaired Technology and Ethics studies for eleven years. Wallach's latest book, a primer on emerging technologies, is entitled, A Dangerous Master: How to keep technology from slipping beyond our control. He co-authored (with Colin Allen) Moral Machines: Teaching Robots Right From Wrong. Wallach has been referred to as, a Godfather of AI Ethics.
Pre-register here (required): https://umaryland.zoom.us/
2026 MIC Symposium on Multilingualism | Featured Talk
Searching for Common Ground in Human-AI Interaction (What Gen AI Can't Do)
Abstract: For many of us, the alien invasion of LLMs has been unsettling, at best. Who knew that monstrous quantities of text and compute power could simulate human discourse processing? But despite claims about emergent theory-of-mind in reasoning models, LLMs remain stochastic parrots: they do not collaborate, project co-presence, form conceptual pacts, accurately signal commitment to what they say, or accrue common ground. This lack of pragmatic competence is clear from our recent studies of narrative and referential communication by AI vs. humans.
Speaker: Susan Brennan is SUNY Distinguished Professor at Stony Brook University in Cognitive Science within the Department of Psychology, with affiliations in Computer Science and Linguistics. Her research uses eye-tracking, experimental, behavioral, and observational methods to study the psycholinguistics of communication (verbal, nonverbal, and multimodal), adaptive language use, and the human use of technology, including spoken dialogue systems. She currently directs the NSF-funded Research Traineeship project, Detecting and Addressing Bias in Data, Humans, and Institutions, a collaboration among PhD. programs in Cognitive Science, Social & Health Psychology, Computer Science, Applied Math & Statistics, Linguistics, Political Science, Economics, Africana Studies, and Women's Studies. This traineeship supports students' AI and bias-related convergent research projects that include assessing potential for LLVMs (Large Language-Vision Models) to act as interlocutors in spontaneous dialogue as well as collaborating with the Innocence Network about AI tools. She holds a Ph.D. in cognitive psychology from Stanford University, an M.S. in visual studies from MIT (from what is now known as the MIT Media Lab), and a B.A. from Cornell University in cultural anthropology. She has worked in industry at Atari Research, Hewlett-Packard Labs, and Apple Computer, as well as at NSF as a program officer for the Graduate Research Funding Program.
Faculty Response Panel:
Location: Humanities 1006
Searching for Common Ground in Human-AI Interaction (What Gen AI Can't Do)
Abstract: For many of us, the alien invasion of LLMs has been unsettling, at best. Who knew that monstrous quantities of text and compute power could simulate human discourse processing? But despite claims about emergent theory-of-mind in reasoning models, LLMs remain stochastic parrots: they do not collaborate, project co-presence, form conceptual pacts, accurately signal commitment to what they say, or accrue common ground. This lack of pragmatic competence is clear from our recent studies of narrative and referential communication by AI vs. humans.
Speaker: Susan Brennan is SUNY Distinguished Professor at Stony Brook University in Cognitive Science within the Department of Psychology, with affiliations in Computer Science and Linguistics. Her research uses eye-tracking, experimental, behavioral, and observational methods to study the psycholinguistics of communication (verbal, nonverbal, and multimodal), adaptive language use, and the human use of technology, including spoken dialogue systems. She currently directs the NSF-funded Research Traineeship project, Detecting and Addressing Bias in Data, Humans, and Institutions, a collaboration among PhD. programs in Cognitive Science, Social & Health Psychology, Computer Science, Applied Math & Statistics, Linguistics, Political Science, Economics, Africana Studies, and Women's Studies. This traineeship supports students' AI and bias-related convergent research projects that include assessing potential for LLVMs (Large Language-Vision Models) to act as interlocutors in spontaneous dialogue as well as collaborating with the Innocence Network about AI tools. She holds a Ph.D. in cognitive psychology from Stanford University, an M.S. in visual studies from MIT (from what is now known as the MIT Media Lab), and a B.A. from Cornell University in cultural anthropology. She has worked in industry at Atari Research, Hewlett-Packard Labs, and Apple Computer, as well as at NSF as a program officer for the Graduate Research Funding Program.
Faculty Response Panel:
- Jiwon Hwang (Panel Chair; Assistant Professor, Asian and Asian American Studies)
- Rose Tirotta-Esposito (Director, Center for Excellence in Learning and Teaching)
- Lav Varshney (Director, AI Innovation Institute)
- Josh Zhang (Associate Professor, Sociology and Institute for Advanced Computational Science)
Location: Humanities 1006
Biomedical Informatics Grand Rounds Talk
Educational objectives:
Speaker: Dr. Janos Hajagos, Ph.D Chief Data Analytics and Research Assistant Professor, Biomedical Informatics, Stony Brook University
Remote Access: Join Zoom meeting
Meeting ID: 95617197636 Passcode: 924293
Educational objectives:
- Explain how generative AI can write data-analysis code from natural-language prompts
- Describe how virtual computational laboratories can be built to explore clinicalquestions
- Identify the key challenges of applying AI models to protected healthcare data
Speaker: Dr. Janos Hajagos, Ph.D Chief Data Analytics and Research Assistant Professor, Biomedical Informatics, Stony Brook University
Remote Access: Join Zoom meeting
Meeting ID: 95617197636 Passcode: 924293