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Longitudinal and Multimodal Recording System to Capture Real-World Patient-Clinician Conversations for AI and Encounter Research: Protocol

Zahidy, Misk Al, Maldonado, Kerly Guevara, Andrango, Luis Vilatuna, Proano, Ana Cristina, Claros, Ana Gabriela, Jimenez, Maria Lizarazo, Toro-Tobon, David, Montori, Victor M., Ponce-Ponte, Oscar J., Brito, Juan P.

arXiv.org Artificial Intelligence

The promise of AI in medicine depends on learning from data that reflect what matters to patients and clinicians. Most existing models are trained on electronic health records (EHRs), which capture biological measures but rarely patient-clinician interactions. These relationships, central to care, unfold across voice, text, and video, yet remain absent from datasets. As a result, AI systems trained solely on EHRs risk perpetuating a narrow biomedical view of medicine and overlooking the lived exchanges that define clinical encounters. Our objective is to design, implement, and evaluate the feasibility of a longitudinal, multimodal system for capturing patient-clinician encounters, linking 360 degree video/audio recordings with surveys and EHR data to create a dataset for AI research. This single site study is in an academic outpatient endocrinology clinic at Mayo Clinic. Adult patients with in-person visits to participating clinicians are invited to enroll. Encounters are recorded with a 360 degree video camera. After each visit, patients complete a survey on empathy, satisfaction, pace, and treatment burden. Demographic and clinical data are extracted from the EHR. Feasibility is assessed using five endpoints: clinician consent, patient consent, recording success, survey completion, and data linkage across modalities. Recruitment began in January 2025. By August 2025, 35 of 36 eligible clinicians (97%) and 212 of 281 approached patients (75%) had consented. Of consented encounters, 162 (76%) had complete recordings and 204 (96%) completed the survey. This study aims to demonstrate the feasibility of a replicable framework for capturing the multimodal dynamics of patient-clinician encounters. By detailing workflows, endpoints, and ethical safeguards, it provides a template for longitudinal datasets and lays the foundation for AI models that incorporate the complexity of care.


Incorporating AI in Diverse Streams of Healthcare

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Artificial intelligence (AI) has emerged as an effective and promising tool in the field of medicine. With improved medical data labeling methods, and enhanced AI-enabled systems, massive amounts of data can be processed quickly, trends can be analyzed, and diseases can be detected and diagnosed more precisely. A positive outcome of incorporating artificial intelligence into healthcare and medical practice is improved patient outcomes and reduced healthcare expenses. The use of artificial intelligence can assist healthcare providers in prompt disease diagnosis, planning the course of treatment, predicting outbreaks of disease, and improving the accuracy of medical predictions. Using AI-based tools, underserved communities can gain access to information and resources otherwise out of reach, bridging the gap between healthcare practitioners and healthcare consumers.


Current Oncology

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Clinical applications of artificial intelligence (AI) in healthcare, including in the field of oncology, have the potential to advance diagnosis and treatment. The literature suggests that patient values should be considered in decision making when using AI in clinical care; however, there is a lack of practical guidance for clinicians on how to approach these conversations and incorporate patient values into clinical decision making. We provide a practical, values-based guide for clinicians to assist in critical reflection and the incorporation of patient values into shared decision making when deciding to use AI in clinical care. Values that are relevant to patients, identified in the literature, include trust, privacy and confidentiality, non-maleficence, safety, accountability, beneficence, autonomy, transparency, compassion, equity, justice, and fairness. The guide offers questions for clinicians to consider when adopting the potential use of AI in their practice; explores illness understanding between the patient and clinician; encourages open dialogue of patient values; reviews all clinically appropriate options; and makes a shared decision of what option best meets the patient’s values. The guide can be used for diverse clinical applications of AI.


Senior Digital Clinical Data Manager

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At Biogen Digital Health (BDH), we aspire to transform Biogen and patients' lives by making personalized & digital medicine in neuroscience a reality. Powered by data-science and digital technologies, we drive solutions to advance research, clinical care, and patient empowerment. Our team strives for real impact through excellence, innovation, and collaboration. This role is of key importance to achieve the strategic vision and objective to make Biogen a recognized leader in digital health sciences, hence contributing to our corporate vision & strategy. At Biogen Digital Health (BDH), we aspire to transform Biogen and patients' lives by making personalized & digital medicine in neuroscience a reality.


Erez Naaman - Digital transformation in diagnostics

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"Together, I think technology and AI will transform the way we diagnose and we treat in the coming years. And this is really enabling clinical care to become very tailor-made, individualized. But even more than that, it enables the patient to get access to the information and the doctors to make better decisions." Intro: Welcome to the Agile Digital Transformation podcast, where we explore different aspects of digital transformation and digital experience with your host, Tim Butara, content and community manager at Agiledrop. Thank you for tuning in.


AI detects Parkinson's disease by tracking your breathing patterns

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A compelling new study indicates Parkinson's disease (PD) could be diagnosed by remotely tracking a person's breathing patterns. Led by researchers from MIT, the study presents an AI system that uses radio waves to monitor breathing while a person sleeps. Dina Katabi, principal investigator on the new research, said the study was inspired by 200-year-old observations from James Parkinson, the first doctor to clinically catalog signs of the degenerative neurological disease. "A relationship between Parkinson's and breathing was noted as early as 1817, in the work of Dr. James Parkinson," explained Katabi. "This motivated us to consider the potential of detecting the disease from one's breathing without looking at movements. Some medical studies have shown that respiratory symptoms manifest years before motor symptoms, meaning that breathing attributes could be promising for risk assessment prior to Parkinson's diagnosis."


Artificial intelligence model can detect Parkinson's from breathing patterns

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Parkinson's disease is notoriously difficult to diagnose as it relies primarily on the appearance of motor symptoms such as tremors, stiffness, and slowness, but these symptoms often appear several years after the disease onset. Now, Dina Katabi, the Thuan (1990) and Nicole Pham Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT and principal investigator at MIT Jameel Clinic, and her team have developed an artificial intelligence model that can detect Parkinson's just from reading a person's breathing patterns. The tool in question is a neural network, a series of connected algorithms that mimic the way a human brain works, capable of assessing whether someone has Parkinson's from their nocturnal breathing -- i.e., breathing patterns that occur while sleeping. The neural network, which was trained by MIT PhD student Yuzhe Yang and postdoc Yuan Yuan, is also able to discern the severity of someone's Parkinson's disease and track the progression of their disease over time. Yang and Yuan are co-first authors on a new paper describing the work, published today in Nature Medicine.


Data Engineer (Mid-Level, Remote)

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Helix is a place where innovators and doers gather in order to drive significant progress in population genomics. We have come together to work at the intersection of clinical care, research, and genomics. If you're excited by the idea of making a meaningful impact and joining a team where we pride ourselves on driving innovation through fostering an environment with an emphasis on empowering one another to grow, Helix might be the place for you! Our end-to-end population genomics platform enables health systems, life sciences companies, and payers to advance genomic research and accelerate the integration of genomic data into routine clinical care. We support all aspects of population genomics from recruitment to translational research and help our partners use genomics to improve health outcomes, increase patient engagement, and lower costs.


Intelligent Medicine

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Improving the speed and accuracy of clinical diagnosis, augmenting clinical decision-making, reducing human error in clinical care, individualizing therapies based on a patient's genomic and metabolomic profiles, differentiating benign from cancerous lesions with impeccable accuracy, identifying likely conditions a person may develop years down the road, spotting early tell-tale signs of an ultrarare disease, intercepting dangerous drug interactions before a patient is given a new medication, yielding real-time insights amidst a raging pandemic to inform optimal treatment of patients infected with a novel human pathogen. These are some of the promises that physicians and researchers look to fulfill using artificial intelligence -- promises poised to transform clinical care, lead to better patient outcomes, and, ultimately, improve human lives. Yet, AI is no silver bullet. It can fall prey to the cognitive fallibilities and blind spots of the humans who design it. AI models can be as imperfect as the data and clinical practices that the machine-learning algorithms are trained on, propagating the very same biases AI was designed to eliminate in the first place. Beyond conceptual and design pitfalls, realizing the potential of AI also requires overcoming systemic hurdles that stand in the way of integrating AI-based technologies into clinical practice.


Putting artificial intelligence at the heart of health care -- with help from MIT

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Artificial intelligence is transforming industries around the world -- and health care is no exception. A recent Mayo Clinic study found that AI-enhanced electrocardiograms (ECGs) have the potential to save lives by speeding diagnosis and treatment in patients with heart failure who are seen in the emergency room. The lead author of the study is Demilade "Demi" Adedinsewo, a noninvasive cardiologist at the Mayo Clinic who is actively integrating the latest AI advancements into cardiac care and drawing largely on her learning experience with MIT Professional Education. A dedicated practitioner, Adedinsewo is a Mayo Clinic Florida Women's Health Scholar and director of research for the Cardiovascular Disease Fellowship program. Her clinical research interests include cardiovascular disease prevention, women's heart health, cardiovascular health disparities, and the use of digital tools in cardiovascular disease management.