Assistant Professor Marzyeh Ghassemi explores how hidden biases in medical data could compromise artificial intelligence approaches. Ghassemi organized MITs first Hacking Discrimination event and was awarded MITs 2018 Seth J. Teller Award for Excellence, Inclusion and Diversity. As an external student: Apply for the Learning to detect vocal hyperfunction from ambulatory necksurface acceleration features: Initial results for vocal fold nodules As an MIT undergrad interested in an UROP: Contact Fern Keniston (fern@csail.mit.edu) to determine if there are research slots available for the semester, and schedule a 30 minute session with Dr. Ghassemi. Professor But that can be deceptive and dangerous, because its harder to ferret out the faulty data supplied en masse to a computer than it is to discount the recommendations of a single possibly inept (and maybe even racist) doctor. S Gaube, H Suresh, M Raue, A Merritt, SJ Berkowitz, E Lermer, Nouvelles citations des articles de cet auteur, Nouveaux articles lis aux travaux de recherche de cet auteur, Professor of Computer Science and Engineering, MIT, Principal Researcher, Microsoft Research Health Futures, Amazon, AIMI (Stanford University), Mila (Quebec AI Institute), Postdoctoral Researcher, Harvard Medical School, Department of Biomedical Informatics, Adresse e-mail valide de hms.harvard.edu, PhD Student (ELLIS, IMPRS-IS), Explainable Machine Learning Group, University of Tuebingen, Adresse e-mail valide de uni-tuebingen.de, Scientist, SickKids Research Institute; Assistant Professor Department of Computer Science, University of Toronto, Assistant Professor, UC Berkeley and UCSF, PhD Student, Massachusetts Institute of Technology, PhD Student, Massachusetts Institute of Technology (MIT), Adresse e-mail valide de cumc.columbia.edu, Adresse e-mail valide de seas.harvard.edu, Director of Voice Science and Technology Laboratory, Center for Laryngeal Surgery and Voice, Harvard Medical School, Massachusetts General Hospital, MGH Institute of Health Professions, Adresse e-mail valide de cs.princeton.edu, Department of Electronic Engineering, Universidad Tcnica Federico Santa Mara, COVID-19 Image Data Collection: Prospective Predictions Are the Future, Do no harm: a roadmap for responsible machine learning for health care, The false hope of current approaches to explainable artificial intelligence in health care, Unfolding Physiological State: Mortality Modelling in Intensive Care Units, A multivariate timeseries modeling approach to severity of illness assessment and forecasting in icu with sparse, heterogeneous clinical data, A Review of Challenges and Opportunities in Machine Learning for Health, Predicting covid-19 pneumonia severity on chest x-ray with deep learning, Clinical Intervention Prediction and Understanding with Deep Neural Networks. [1][2][3], In 2012, Ghassemi was a member of the Sana AudioPulse team, who won the GSMA Mobile Health Challenge as a result of developing a mobile phone app to screen for hearing impairment remotely. She has also organized and MITs first WebMachine learning for health must be reproducible to ensure reliable clinical use. This page was last edited on 19 March 2023, at 11:56. Colak, E., Moreland, R., Ghassemi, M. (2021). It all comes down to data, given that the AI tools in question train themselves by processing and analyzing vast quantities of data. Human caregivers generate bad data sometimes because they are not perfect., Nevertheless, she still believes that machine learning can offer benefits in health care in terms of more efficient and fairer recommendations and practices. WebWhy aren't mistakes always a bad thing? Cohen, J. P., Morrison, P., Dao, L., Roth, K., Duong, T. Q., Ghassemi, M. (2020). Healthy ML Clinical Inference Machine Learning. Read more about our During 20122013, she was one of MITs GSC Housing Community Activities Family Subcommittee Leads, and campaigned to have back-up childcare options extended to all graduate students at MIT. We evaluated 511 scientific papers across several machine learning subfields and found that machine learning for health compared poorly to other areas regarding reproducibility metrics, such as dataset and code accessibility. Challenges to the Reproducibility of Machine Learning Models in Health Care. degree in biomedical engineering from Oxford University as a Marshall Scholar, and B.S. WebMarzyeh Ghassemi is an assistant professor at MIT in the Department of Electrical Engineering and Computer Science and at the Institute for Medical Engineering Five principles for the intelligent use of AI in medical imaging. We evaluated 511 scientific papers across several machine learning subfields and found that machine learning for health compared poorly to other areas regarding reproducibility metrics, such as dataset and code accessi Upon a closer look, she saw that models often worked differently specifically worse for populations including Black women, a revelation that took her by surprise. Les articles suivants sont fusionns dans GoogleScholar. In 2015, she also worked as a graduate student member of MITs CJAC (Corporation Joint Advisory Committee on Institute-wide Affairs), a committee to which the Corporation can turn for consideration and advice on special Institute-wide issues. Theres also the matter of who will collect it and vet it. degree in biomedical engineering from Oxford University as a Marshall Scholar, and B.S. AMIA is grateful to the Charter Donors who offered support for the fund in its formative period (between the AMIA Symposium in 2015 and March 2017). degree in biomedical engineering from Oxford University as a Marshall Scholar. Models must also be healthy, in that they should not learn biased rules or recommendations that harm minorities or minoritized populations. Do Eric benet and Lisa bonet have a child together? Mobility-related data show the pandemic has had a lasting effect, limiting the breadth of places people visit in cities. Translational psychiatry 6 (10), e921-e921, Can AI Help Reduce Disparities in General Medical and Mental Health Care? J Wiens, S Saria, M Sendak, M Ghassemi, VX Liu, F Doshi-Velez, K Jung, They just need to be cognizant of the gaps that appear in treatment and other complexities that ought to be considered before giving their stamp of approval to a particular computer model.. On leave. Hidden biases in medical data could compromise AI approaches to healthcare. Marzyeh Ghassemiwill join the Institute for Medical Engineering and Science and the Department of Electrical Engineering and Computer Science as an Assistant Professor in July. Marzyeh Ghassemi is an Assistant Professor at MIT in Electrical Engineering and Computer Science (EECS) and Institute for Medical Engineering & Science (IMES), and a Can AI Help Reduce Disparities in General Medical and Mental Health Care? Professor Ghassemi has published across computer science and clinical venues, including NeurIPS, KDD, AAAI, MLHC, JAMIA, JMIR, JMLR, AMIA-CRI, Nature Medicine, Nature Translational Psychiatry, and Critical Care. WebMarzyeh Ghassemi, Leo Anthony Celi and David J Stone Critical Care 2015, vol 19, no. It wasnt until the end of my PhD work that one of my committee members asked: Did you ever check to see how well your model worked across different groups of people?, That question was eye-opening for Ghassemi, who had previously assessed the performance of models in aggregate, across all patients. degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University, worked at Intel Corporation, and received an MSc. Our team uses accelerometers and machine learning to help detect vocal disorders. Prior to her PhD in Computer Science at MIT, she received an MSc. KDD 2014, A multivariate timeseries modeling approach to severity of illness assessment and forecasting in icu with sparse, heterogeneous clinical data 192 2015 The promise and pitfalls of artificial intelligence explored at TEDxMIT event, Machine-learning system flags remedies that might do more harm than good, The potential of artificial intelligence to bring equity in health care, One-stop machine learning platform turns health care data into insights, Study finds gender and skin-type bias in commercial artificial-intelligence systems, More about MIT News at Massachusetts Institute of Technology, Abdul Latif Jameel Poverty Action Lab (J-PAL), Picower Institute for Learning and Memory, School of Humanities, Arts, and Social Sciences, View all news coverage of MIT in the media, Paper: "In Medicine, How Do We Machine Learn Anything Real? +1-617-253-3291, Electrical Engineering and Computer Science, Institute for Medical Engineering and Science. by Steve Nadis, Massachusetts Institute of Technology. Her work has been featured in popular press such as Fortune, MIT News, NVIDIA, and The Huffington Post. Published February 2, 2022 By Mehdi Fatemi , Senior Researcher Taylor Killian , PhD student Marzyeh Ghassemi , Assistant Professor As the pandemic overburdens medical facilities and clinicians become increasingly overworked, the ability to make quick decisions on providing the best possible treatment is even more critical. Doctors know what it means to be sick, Ghassemi explains, and we have the most data for people when they are sickest. I don't know where they were born but I do know what year they were born inJasmine was born in1999Nicolas was born in 1995Saveria was born in 1997Hayden was born in 1996Tyler was born in 1998Diane was born in 1997Jaydee-Lynn was born in 1996. WebDr. Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings. Prior to her PhD in Computer Science at MIT, she received an MSc. WebAU - Ghassemi, Marzyeh. This answer is: MIT School of Engineering Using ambulatory voice monitoring to investigate common voice disorders: Research update, MS, Biomedical Engineering, Oxford University, 2011, Sept 2021 Herman L. F. von Helmholtz Career Development Professorship, MIT, July 2020 Azrieli Global Scholar, CIFARs Program in Learning in Machines and Brains, Oct. 2018 35 Innovators Under 35 Award, MIT Technology Review, MIT HST.953: Clinical Data Learning, Fall 2021, Fall 2022, MIT EECS 6.882: Ethical Machine Learning in Human Deployments, Spring 2022. From 2013-2014, she was a student representative on MITs Womens Advisory Group Presidential Committee, and additionally was elected as a Graduate Student Council (GSC) Housing Community Activities Co-Chair. Professor Ghassemi has previously served as a NeurIPS Workshop Co-Chair and General Chair for the ACM Conference on Health, Inference and Learning (CHIL). The event still happens every Monday in CSAIL. We really need to collect this data and audit it., The challenge here is that the collection of data is not incentivized or rewarded, she notes. Leveraging a critical care database: SSRI use prior to ICU admission is associated with increased hospital mortality. WebMarzyeh Ghassemi. The program is now fully funded by MIT, and considered a success. Marzyeh Ghassemi. Annual Update in Intensive Care and Emergency Medicine 2015, 573-586, Predicting early psychiatric readmission with natural language processing of narrative discharge summaries 95 2016 All Rights Reserved. Its people. She is currently an assistant professor at the University of Toronto's Department of Computer Science and Faculty of Medicine, and is a Canada CIFAR Artificial Intelligence (AI) chair and Canada Research Chair (Tier Two) in machine learning for health. Professor Ghassemi has published across computer science and clinical venues, including NeurIPS, KDD, AAAI, MLHC, JAMIA, JMIR, JMLR, AMIA-CRI, Nature Medicine, Nature Translational Psychiatry, and Critical Care. Open Mic session on "Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data". degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University. Canada-based researcher in the field of computational medicine, Computer Science and Artificial Intelligence Lab, Journal of the American Medical Informatics Association, Frontiers in Bioengineering and Biotechnology, "New U of T researcher named to magazine's 'Innovators under 35' list", "Marzyeh Ghassemi is using AI to make sense of messy hospital data", "Sana AudioPulse wins Mobile Health Challenge", "Innovators, Entrepreneurs, Pioneers | Best Innovators Under 35", "Who are the new U of T Vector Institute researchers? degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University, worked at Intel Corporation, and received an MSc. Computer Science & Artificial Intelligence Laboratory. She holds MIT affiliations with the Jameel Clinic and CSAIL. 2014-05-24 01:29:44. Marzyeh Ghassemi is a Canada-based researcher in the field of computational medicine, where her research focuses on developing machine-learning algorithms to inform health-care decisions. Language links are at the top of the page across from the title. She has also organized and MITs first Hacking Discrimination event, and was awarded MITs 2018 Seth J. Teller Award for Excellence, Inclusion and Diversity. 77 Massachusetts Ave. The HealthyML has demonstrated that naive application of state-of-the-art techniques likedifferentially private machine learning cause minority groups to lose predictive influence in health tasks. A reviewled Prof. Marzyeh Ghassemi has found that a major issue in health-related machine learning models is the relative scarcity of publicly available data sets in medicine, reports Emily Sohn for Nature. Predicting early psychiatric readmission with natural language processing of narrative discharge summaries. [11][16][17] In June 2019, Ghassemi was appointed a Canada Research Chair (Tier Two) in machine learning for health. The Huffington Post. Jake Albrecht (Sage Bionetworks) Marco Ciccone (Politecnico di Torino) Tao Qin (Microsoft Research) Datasets and Benchmarks Chair. But does that really show that medical treatment itself is free from bias? [2][6][11][12][13] Ghassemi's lab is titled the Machine Learning for Health (ML4H) lab. Tutorial on "Inductive Data Investigation: From ugly clinical data to KDD 2014". Ghassemis work has been published in topconferencesand journals includingNeurIPS, FaCCT,The Lancet Digital Health,JAMA, theAMA Journal of Ethics, andNature Medicine, and featured in popular press such as MIT News, NVIDIA, and the Huffington Post. WebMarzyeh Ghassemi (MIT) Saadia Gabriel (University of Washington) Competition Chair. Professor Ghassemi is on the Senior Advisory Council of Women in Machine Learning (WiML), and organized its flagship workshop at NIPS during December 2014. Simultaneous Similarity-based Self-Distillation for Deep Metric Learning, A comprehensive EHR timeseries pre-training benchmark, An empirical framework for domain generalization in clinical settings. When was AR 15 oralite-eng co code 1135-1673 manufactured? 90 2019 Similarly, women face increased risks during metal-on-metal hip replacements, Ghassemi and Nsoesie write, due in part to anatomic differences that arent taken into account in implant design. Facts like these could be buried within the data fed to computer models whose output will be undermined as a result. She will join the University of Toronto as an Assistant Professor in Computer Science and Medicine in Fall 2018, and will be affiliated with, Her work has appeared in KDD, AAAI, IEEE TBME, MLHC, JAMIA, and AMIA-CRI; she has also. Short-Term Mortality Prediction for Elderly Dr. Marzyeh Ghassemi is an Assistant Professor at MIT in Electrical Engineering and Computer Science (EECS) and Institute for Medical Engineering & Following the publication of the original article [], we were notified that current affiliations 17, 18 and 19 were erroneously added to the first author rather than the senior author (Marzyeh Ghassemi). First Place winner at MIT Sloan-ILP Innovators Showcase, written up by the Boston Business Journal. Previously, she was a Visiting Researcher with Alphabets Verily. While working toward her dissertation in computer science at MIT, Marzyeh Ghassemi wrote several papers on how machine-learning techniques from artificial intelligence could be applied to clinical data in order to predict patient outcomes. Marzyehs research focuses on machine learning with clinical data to predict and stratify relevant human risks, encompassing unsupervised learning, supervised learning, structured prediction. Pulse oximeters, for example, which have been calibrated predominately on light-skinned individuals, do not accurately measure blood oxygen levels for people with darker skin. degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University. The Lancet Digital Health 3 (11), e745-e750. Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L. Beam, Irene Y. Chen, Rajesh Ranganath Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. We find that race, even in the great equalizer of end-of-life care, does continue to influence the treatments administered to a patient. Representation Learning, Behavioral ML, Healthcare ML, Healthy ML, COVID-19 Image Data Collection: Prospective Predictions Are the Future 660 2020, JP Cohen, P Morrison, L Dao, K Roth, TQ Duong, M Ghassemi Zhang, H., Dullerud, N., Seyyed-Kalantari, L., Morris, Q., Joshi, S., Ghassemi, M. (2021). She is currently on leave from the University of Toronto Departments of Computer Science and Medicine. Room 1-206 I hadnt made the connection beforehand that health disparities would translate directly to model disparities, she says. She also founded the non-profit Association for Health Learning and Inference. WebDr. Engineering & Science WebMarzyeh Ghassemi is an assistant professor and the Hermann L. F. von Helmholtz Professor with appointments in the Department of Electrical Engineering and Computer Aug Our analysis agrees with previous studies that nonwhites tend to receive more aggressive (high-risk, high reward) treatments, such as mechanical ventilation than non-whites, despite receiving comparable-or-moderately-less noninvasive treatments. Marzyeh has a well-established academic track record across computer science and clinical venues, including NeurIPS, KDD, AAAI, MLHC, JAMIA, JMIR, JMLR, AMIA-CRI, EMBC, Nature Medicine, Nature Translational Psychiatry, and Critical Care. When you take state-of-the-art machine learning methods and systems and then evaluate them on different patient groups, they do not perform equally, says Ghassemi. Twenty-Ninth AAAI Conference on Artificial Intelligence, Do no harm: a roadmap for responsible machine learning for health care 164 2019 Ghassemi recommends assembling diverse groups of researchers clinicians, statisticians, medical ethicists, and computer scientists to first gather diverse patient data and then focus on developing fair and equitable improvements in health care that can be deployed in not just one advanced medical setting, but in a wide range of medical settings., The objective of the Patterns paper is not to discourage technologists from bringing their expertise in machine learning to the medical world, she says. G Liu, TMH Hsu, M McDermott, W Boag, WH Weng, P Szolovits, Machine Learning for Healthcare Conference, 249-269, A Raghu, M Komorowski, I Ahmed, L Celi, P Szolovits, M Ghassemi. Doctors trained at the same medical school for 10 years can, and often do, disagree about a patients diagnosis, Ghassemi says. She was also recently named one of MIT Tech Reviews 35 Innovators Under 35. M Ghassemi, MAF Pimentel, T Naumann, T Brennan, DA Clifton, However, we still dont fundamentally understand what it means to be healthy, and the same patient may receive different treatments across different hospitals or clinicians as new evidence is discovered, or individual illness is interpreted. Using ambulatory voice monitoring to investigate common voice disorders: Research update. Marzyeh Ghassemi. The Healthy ML group tackles the many novel technical opportunities for machine learning in health, and works to make important progress with careful application to this domain. What is the cast of surname sable in maharashtra? A full list of Professor Ghassemis publications can be found here. While working toward her dissertation in computer science at MIT, Marzyeh Ghassemi wrote several papers on how machine-learning techniques from artificial [18] Ghassemi has been cited over 1900 times, and has an h-index and i-10 index of 23 and 36 respectively. The research center will support two nonprofits and four government agencies in designing randomized evaluations on housing stability, procedural justice, transportation, income assistance, and more. [3][5] She then developed machine-learning algorithms to take in diverse clinical inputs and predict risks and mortality, such as the length of the patient's stay within the hospital, and whether additional interventions (such as blood transfusions) are necessary. Marzyeh completed her PhD at MIT where her research focused on machine learning in health care, exploring how to WebMarzyeh Ghassemi, PhD1, Tristan Naumann, PhD2, Peter Schulam, PhD3, Andrew L. Beam, PhD4, Irene Y. Chen, SM5, Rajesh Ranganath, PhD6 1University of Toronto and Vector Institute, Toronto, Canada; 2Microsoft Research, Redmond, WA, USA; 3Johns Hopkins University, Baltimore, MD, USA; 4Harvard School of Public Health, Boston, MA, M Ghassemi, LA Celi, DJ Stone A campus summit with the leader and his delegation centered around dialogue on biotechnology and innovation ecosystems. Marzyeh Ghassemi is a Canada-based researcher in the field of computational medicine, where her research focuses on developing machine-learning algorithms to inform health-care decisions. IY Chen, P Szolovits, M Ghassemi WebMarzyeh Ghassemi (MIT) Saadia Gabriel (University of Washington) Competition Chair. Health is important, and improvements in health improve lives. Her work has been featured in popular press such as MIT News, NVIDIA, Huffington Post. The growing data in EHRs makes healthcare ripe for the use of machine learning. A Rumshisky, M Ghassemi, T Naumann, P Szolovits, VM Castro, Machine Learning for Healthcare Conference, 147-163, State of the art review: the data revolution in critical care 99 2015 In 2015, she also worked as a graduate student member of MITs CJAC (Corporation Joint Advisory Committee on Institute-wide Affairs), a committee to which the Corporation can turn for consideration and advice on special Institute-wide issues. Marzyeh is on the Senior Advisory Council of Women in Machine Learning (WiML), and organized its flagship workshop at NIPS during December 2014. First Place winner at the 2012 GSMA Mobile Health Student Challenge in Cape Town! A new method could provide detailed information about internal structures, voids, and cracks, based solely on data about exterior conditions. Professor Ghassemi holds a Herman L. F. von Helmholtz Career Development Professorship, and was named a CIFAR Azrieli Global Scholar and one of MIT Tech Reviews 35 Innovators Under 35. This website is managed by the MIT News Office, part of the Institute Office of Communications. The Campaign was chaired by Dr. Ted Shortliffe (who also offered a 1:1 match for all donations up to Prior to MIT, Marzyeh received B.S. WebMarzyeh Ghassemi University of Toronto Vector Institute Abstract Models that perform well on a training do-main often fail to generalize to out-of-domain (OOD) examples. Prof. Marzyeh Ghassemi speaks with WBUR reporter Geoff Brumfiel about her research studying the use of artificial intelligence in healthcare. This led the GSC to commit $30,000 to a pilot for the program, which was matched by the administration. [19] She was named as one of the 35 Innovators Under 35, in the visionaries category, in MIT Technology Review's annual list.[2][3]. [9], Upon completing her PhD, Ghassemi was affiliated with both Alphabets Verily (as a visiting researcher) and at MIT (as a part-time post-doctoral researcher in Peter Szolovits' Computer Science and Artificial Intelligence Lab). degree in biomedical engineering from Oxford University as a Marshall Scholar, and B.S. Presentation on "Estimating the Response and Effect of Clinical Interventions". Download PDF. Copyright 2023 Marzyeh Ghassemi. Even mechanical devices can contribute to flawed data and disparities in treatment. Understanding vasopressor intervention and weaning: Risk prediction in a public heterogeneous clinical time series database. Thats different from the applications where existing machine-learning algorithms excel like object-recognition tasks because practically everyone in the world will agree that a dog is, in fact, a dog. AI in health and medicine. Frontiers in bioengineering and biotechnology 3, 155. Marzyeh Ghassemi is an Assistant Professor at the University of Toronto in Computer Science and Medicine, and a Vector Institute faculty member holding a Canadian CIFAR Website Google Scholar. Marzyeh Ghassemi is a Visiting Researcher with Googles Verily and a post-doc in the Clinical Decision Making Group at MITs Computer Science and Artificial Intelligence Lab (CSAIL) supervised by Dr. Peter Szolovits. NVIDIA, and We focus on furthering the application of technology and artificial intelligence in medicine and health-care. Did Billy Graham speak to Marilyn Monroe about Jesus? Machine learning for health must be reproducible to ensure reliable clinical use. Cambridge, MA 02139. Copy. And data providers might say, Why should I give my data out for free when I can sell it to a company for millions? But researchers should be able to access data without having to deal with questions like: What paper will I get my name on in exchange for giving you access to data that sits at my institution?, The only way to get better health care is to get better data, Ghassemi says, and the only way to get better data is to incentivize its release., Its not only a question of collecting data. Physicians, however, dont always concur on the rules for treating patients, and even the win condition of being healthy is not widely agreed upon. Going further, we show that using treatment patterns and clinical notes, we are able to infer a patient's race. Previously, she was a Visiting Researcher with Alphabets Verily and an Assistant Professor at University of Toronto. Finally, we show evidence suggesting nonwhite have a much greater distrust of the medical community among than whites do. M Ghassemi, T Naumann, F Doshi-Velez, N Brimmer, R Joshi, M Ghassemi, MAF Pimentel, T Naumann, T Brennan, DA Clifton, Twenty-Ninth AAAI Conference on Artificial Intelligence, M Ghassemi, T Naumann, P Schulam, AL Beam, IY Chen, R Ranganath, AMIA Summits on Translational Science Proceedings 191. Prior to her PhD in Computer Science at MIT, she received an MSc. AMA Journal of Ethics 21 (2), 167-179, Using ambulatory voice monitoring to investigate common voice disorders: Research update WebMarzyeh Ghassemi is a Canada -based researcher in the field of computational medicine, where her research focuses on developing machine-learning algorithms to inform health-care decisions. Jake Albrecht (Sage Bionetworks) Marco Ciccone (Politecnico di Torino) Tao Qin (Microsoft Research) Datasets and Benchmarks Chair. Invited Talk on "Physiological Acuity Modelling with (Ugly) Temporal Clinical Data", First place winner of the MIT $100K Accelerate $10,000 Daniel M. Lewin Accelerate Prize. Pranav Rajpurkar, Emma Chen, Eric J. Topol. Les, Le dcompte "Cite par" inclut les citations des articles suivants dans GoogleScholar. Coming from computers, the product of machine-learning algorithms offers the sheen of objectivity, according to Ghassemi. We capture data about the motions of patient's vocal folds to determine if their vocal behavior is normal or abnormal. Can AI Help Reduce Disparities in General Medical and Mental Health Care? But we dont get much data from people when they are healthy because theyre less likely to see doctors then.. Prof. Marzyeh Ghassemi speaks with WBUR reporter Geoff Brumfiel about her research studying the use of artificial intelligence in healthcare. And given that I am a visible minority woman-identifying computer scientist at MIT, I am reasonably certain that many others werent aware of this either., In a paper published Jan. 14 in the journal Patterns, Ghassemi who earned her doctorate in 2017 and is now an assistant professor in the Department of Electrical Engineering and Computer Science and the MIT Institute for Medical Engineering and Science (IMES) and her coauthor, Elaine Okanyene Nsoesie of Boston University, offer a cautionary note about the prospects for AI in medicine.
