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    AI model identifies certain breast tumor stages likely to progress to invasive cancer

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    Ductal carcinoma in situ (DCIS) is a type of preinvasive tumor that sometimes progresses to a highly deadly form of breast cancer. It accounts for about 25 percent of all breast cancer diagnoses.Because it is difficult for clinicians to determine the type and stage of DCIS, patients with DCIS are often overtreated. To address this, an interdisciplinary team of researchers from MIT and ETH Zurich developed an AI model that can identify the different stages of DCIS from a cheap and easy-to-obtain breast tissue image. Their model shows that both the state and arrangement of cells in a tissue sample are important for determining the stage of DCIS.Because such tissue images are so easy to obtain, the researchers were able to build one of the largest datasets of its kind, which they used to train and test their model. When they compared its predictions to conclusions of a pathologist, they found clear agreement in many instances.In the future, the model could be used as a tool to help clinicians streamline the diagnosis of simpler cases without the need for labor-intensive tests, giving them more time to evaluate cases where it is less clear if DCIS will become invasive.“We took the first step in understanding that we should be looking at the spatial organization of cells when diagnosing DCIS, and now we have developed a technique that is scalable. From here, we really need a prospective study. Working with a hospital and getting this all the way to the clinic will be an important step forward,” says Caroline Uhler, a professor in the Department of Electrical Engineering and Computer Science (EECS) and the Institute for Data, Systems, and Society (IDSS), who is also director of the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard and a researcher at MIT’s Laboratory for Information and Decision Systems (LIDS).Uhler, co-corresponding author of a paper on this research, is joined by lead author Xinyi Zhang, a graduate student in EECS and the Eric and Wendy Schmidt Center; co-corresponding author GV Shivashankar, professor of mechogenomics at ETH Zurich jointly with the Paul Scherrer Institute; and others at MIT, ETH Zurich, and the University of Palermo in Italy. The open-access research was published July 20 in Nature Communications.Combining imaging with AI   Between 30 and 50 percent of patients with DCIS develop a highly invasive stage of cancer, but researchers don’t know the biomarkers that could tell a clinician which tumors will progress.Researchers can use techniques like multiplexed staining or single-cell RNA sequencing to determine the stage of DCIS in tissue samples. However, these tests are too expensive to be performed widely, Shivashankar explains.In previous work, these researchers showed that a cheap imagining technique known as chromatin staining could be as informative as the much costlier single-cell RNA sequencing.For this research, they hypothesized that combining this single stain with a carefully designed machine-learning model could provide the same information about cancer stage as costlier techniques.First, they created a dataset containing 560 tissue sample images from 122 patients at three different stages of disease. They used this dataset to train an AI model that learns a representation of the state of each cell in a tissue sample image, which it uses to infer the stage of a patient’s cancer.However, not every cell is indicative of cancer, so the researchers had to aggregate them in a meaningful way.They designed the model to create clusters of cells in similar states, identifying eight states that are important markers of DCIS. Some cell states are more indicative of invasive cancer than others. The model determines the proportion of cells in each state in a tissue sample.Organization matters“But in cancer, the organization of cells also changes. We found that just having the proportions of cells in every state is not enough. You also need to understand how the cells are organized,” says Shivashankar.With this insight, they designed the model to consider proportion and arrangement of cell states, which significantly boosted its accuracy.“The interesting thing for us was seeing how much spatial organization matters. Previous studies had shown that cells which are close to the breast duct are important. But it is also important to consider which cells are close to which other cells,” says Zhang.When they compared the results of their model with samples evaluated by a pathologist, it had clear agreement in many instances. In cases that were not as clear-cut, the model could provide information about features in a tissue sample, like the organization of cells, that a pathologist could use in decision-making.This versatile model could also be adapted for use in other types of cancer, or even neurodegenerative conditions, which is one area the researchers are also currently exploring.“We have shown that, with the right AI techniques, this simple stain can be very powerful. There is still much more research to do, but we need to take the organization of cells into account in more of our studies,” Uhler says.This research was funded, in part, by the Eric and Wendy Schmidt Center at the Broad Institute, ETH Zurich, the Paul Scherrer Institute, the Swiss National Science Foundation, the U.S. National Institutes of Health, the U.S. Office of Naval Research, the MIT Jameel Clinic for Machine Learning and Health, the MIT-IBM Watson AI Lab, and a Simons Investigator Award. More

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      MIT-Takeda Program wraps up with 16 publications, a patent, and nearly two dozen projects completed

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      When the Takeda Pharmaceutical Co. and the MIT School of Engineering launched their collaboration focused on artificial intelligence in health care and drug development in February 2020, society was on the cusp of a globe-altering pandemic and AI was far from the buzzword it is today.As the program concludes, the world looks very different. AI has become a transformative technology across industries including health care and pharmaceuticals, while the pandemic has altered the way many businesses approach health care and changed how they develop and sell medicines.For both MIT and Takeda, the program has been a game-changer.When it launched, the collaborators hoped the program would help solve tangible, real-world problems. By its end, the program has yielded a catalog of new research papers, discoveries, and lessons learned, including a patent for a system that could improve the manufacturing of small-molecule medicines.Ultimately, the program allowed both entities to create a foundation for a world where AI and machine learning play a pivotal role in medicine, leveraging Takeda’s expertise in biopharmaceuticals and the MIT researchers’ deep understanding of AI and machine learning.“The MIT-Takeda Program has been tremendously impactful and is a shining example of what can be accomplished when experts in industry and academia work together to develop solutions,” says Anantha Chandrakasan, MIT’s chief innovation and strategy officer, dean of the School of Engineering, and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “In addition to resulting in research that has advanced how we use AI and machine learning in health care, the program has opened up new opportunities for MIT faculty and students through fellowships, funding, and networking.”What made the program unique was that it was centered around several concrete challenges spanning drug development that Takeda needed help addressing. MIT faculty had the opportunity to select the projects based on their area of expertise and general interest, allowing them to explore new areas within health care and drug development.“It was focused on Takeda’s toughest business problems,” says Anne Heatherington, Takeda’s research and development chief data and technology officer and head of its Data Sciences Institute.“They were problems that colleagues were really struggling with on the ground,” adds Simon Davies, the executive director of the MIT-Takeda Program and Takeda’s global head of statistical and quantitative sciences. Takeda saw an opportunity to collaborate with MIT’s world-class researchers, who were working only a few blocks away. Takeda, a global pharmaceutical company with global headquarters in Japan, has its global business units and R&D center just down the street from the Institute.As part of the program, MIT faculty were able to select what issues they were interested in working on from a group of potential Takeda projects. Then, collaborative teams including MIT researchers and Takeda employees approached research questions in two rounds. Over the course of the program, collaborators worked on 22 projects focused on topics including drug discovery and research, clinical drug development, and pharmaceutical manufacturing. Over 80 MIT students and faculty joined more than 125 Takeda researchers and staff on teams addressing these research questions.The projects centered around not only hard problems, but also the potential for solutions to scale within Takeda or within the biopharmaceutical industry more broadly.Some of the program’s findings have already resulted in wider studies. One group’s results, for instance, showed that using artificial intelligence to analyze speech may allow for earlier detection of frontotemporal dementia, while making that diagnosis more quickly and inexpensively. Similar algorithmic analyses of speech in patients diagnosed with ALS may also help clinicians understand the progression of that disease. Takeda is continuing to test both AI applications.Other discoveries and AI models that resulted from the program’s research have already had an impact. Using a physical model and AI learning algorithms can help detect particle size, mix, and consistency for powdered, small-molecule medicines, for instance, speeding up production timelines. Based on their research under the program, collaborators have filed for a patent for that technology.For injectable medicines like vaccines, AI-enabled inspections can also reduce process time and false rejection rates. Replacing human visual inspections with AI processes has already shown measurable impact for the pharmaceutical company.Heatherington adds, “our lessons learned are really setting the stage for what we’re doing next, really embedding AI and gen-AI [generative AI] into everything that we do moving forward.”Over the course of the program, more than 150 Takeda researchers and staff also participated in educational programming organized by the Abdul Latif Jameel Clinic for Machine Learning in Health. In addition to providing research opportunities, the program funded 10 students through SuperUROP, the Advanced Undergraduate Research Opportunities Program, as well as two cohorts from the DHIVE health-care innovation program, part of the MIT Sandbox Innovation Fund Program.Though the formal program has ended, certain aspects of the collaboration will continue, such as the MIT-Takeda Fellows, which supports graduate students as they pursue groundbreaking research related to health and AI. During its run, the program supported 44 MIT-Takeda Fellows and will continue to support MIT students through an endowment fund. Organic collaboration between MIT and Takeda researchers will also carry forward. And the programs’ collaborators are working to create a model for similar academic and industry partnerships to widen the impact of this first-of-its-kind collaboration.  More

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      A data-driven approach to making better choices

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      Imagine a world in which some important decision — a judge’s sentencing recommendation, a child’s treatment protocol, which person or business should receive a loan — was made more reliable because a well-designed algorithm helped a key decision-maker arrive at a better choice. A new MIT economics course is investigating these interesting possibilities.Class 14.163 (Algorithms and Behavioral Science) is a new cross-disciplinary course focused on behavioral economics, which studies the cognitive capacities and limitations of human beings. The course was co-taught this past spring by assistant professor of economics Ashesh Rambachan and visiting lecturer Sendhil Mullainathan.Rambachan studies the economic applications of machine learning, focusing on algorithmic tools that drive decision-making in the criminal justice system and consumer lending markets. He also develops methods for determining causation using cross-sectional and dynamic data.Mullainathan will soon join the MIT departments of Electrical Engineering and Computer Science and Economics as a professor. His research uses machine learning to understand complex problems in human behavior, social policy, and medicine. Mullainathan co-founded the Abdul Latif Jameel Poverty Action Lab (J-PAL) in 2003.The new course’s goals are both scientific (to understand people) and policy-driven (to improve society by improving decisions). Rambachan believes that machine-learning algorithms provide new tools for both the scientific and applied goals of behavioral economics.“The course investigates the deployment of computer science, artificial intelligence (AI), economics, and machine learning in service of improved outcomes and reduced instances of bias in decision-making,” Rambachan says.There are opportunities, Rambachan believes, for constantly evolving digital tools like AI, machine learning, and large language models (LLMs) to help reshape everything from discriminatory practices in criminal sentencing to health-care outcomes among underserved populations.Students learn how to use machine learning tools with three main objectives: to understand what they do and how they do it, to formalize behavioral economics insights so they compose well within machine learning tools, and to understand areas and topics where the integration of behavioral economics and algorithmic tools might be most fruitful.Students also produce ideas, develop associated research, and see the bigger picture. They’re led to understand where an insight fits and see where the broader research agenda is leading. Participants can think critically about what supervised LLMs can (and cannot) do, to understand how to integrate those capacities with the models and insights of behavioral economics, and to recognize the most fruitful areas for the application of what investigations uncover.The dangers of subjectivity and biasAccording to Rambachan, behavioral economics acknowledges that biases and mistakes exist throughout our choices, even absent algorithms. “The data used by our algorithms exist outside computer science and machine learning, and instead are often produced by people,” he continues. “Understanding behavioral economics is therefore essential to understanding the effects of algorithms and how to better build them.”Rambachan sought to make the course accessible regardless of attendees’ academic backgrounds. The class included advanced degree students from a variety of disciplines.By offering students a cross-disciplinary, data-driven approach to investigating and discovering ways in which algorithms might improve problem-solving and decision-making, Rambachan hopes to build a foundation on which to redesign existing systems of jurisprudence, health care, consumer lending, and industry, to name a few areas.“Understanding how data are generated can help us understand bias,” Rambachan says. “We can ask questions about producing a better outcome than what currently exists.”Useful tools for re-imagining social operationsEconomics doctoral student Jimmy Lin was skeptical about the claims Rambachan and Mullainathan made when the class began, but changed his mind as the course continued.“Ashesh and Sendhil started with two provocative claims: The future of behavioral science research will not exist without AI, and the future of AI research will not exist without behavioral science,” Lin says. “Over the course of the semester, they deepened my understanding of both fields and walked us through numerous examples of how economics informed AI research and vice versa.”Lin, who’d previously done research in computational biology, praised the instructors’ emphasis on the importance of a “producer mindset,” thinking about the next decade of research rather than the previous decade. “That’s especially important in an area as interdisciplinary and fast-moving as the intersection of AI and economics — there isn’t an old established literature, so you’re forced to ask new questions, invent new methods, and create new bridges,” he says.The speed of change to which Lin alludes is a draw for him, too. “We’re seeing black-box AI methods facilitate breakthroughs in math, biology, physics, and other scientific disciplines,” Lin  says. “AI can change the way we approach intellectual discovery as researchers.”An interdisciplinary future for economics and social systemsStudying traditional economic tools and enhancing their value with AI may yield game-changing shifts in how institutions and organizations teach and empower leaders to make choices.“We’re learning to track shifts, to adjust frameworks and better understand how to deploy tools in service of a common language,” Rambachan says. “We must continually interrogate the intersection of human judgment, algorithms, AI, machine learning, and LLMs.”Lin enthusiastically recommended the course regardless of students’ backgrounds. “Anyone broadly interested in algorithms in society, applications of AI across academic disciplines, or AI as a paradigm for scientific discovery should take this class,” he says. “Every lecture felt like a goldmine of perspectives on research, novel application areas, and inspiration on how to produce new, exciting ideas.”The course, Rambachan says, argues that better-built algorithms can improve decision-making across disciplines. “By building connections between economics, computer science, and machine learning, perhaps we can automate the best of human choices to improve outcomes while minimizing or eliminating the worst,” he says.Lin remains excited about the course’s as-yet unexplored possibilities. “It’s a class that makes you excited about the future of research and your own role in it,” he says. 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      Growing our donated organ supply

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      For those in need of one, an organ transplant is a matter of life and death. 

      Every year, the medical procedure gives thousands of people with advanced or end-stage diseases extended life. This “second chance” is heavily dependent on the availability, compatibility, and proximity of a precious resource that can’t be simply bought, grown, or manufactured — at least not yet.

      Instead, organs must be given — cut from one body and implanted into another. And because living organ donation is only viable in certain cases, many organs are only available for donation after the donor’s death.

      Unsurprisingly, the logistical and ethical complexity of distributing a limited number of transplant organs to a growing wait list of patients has received much attention. There’s an important part of the process that has received less focus, however, and which may hold significant untapped potential: organ procurement itself.

      “If you have a donated organ, who should you give it to? This question has been extensively studied in operations research, economics, and even applied computer science,” says Hammaad Adam, a graduate student in the Social and Engineering Systems (SES) doctoral program at the MIT Institute for Data, Systems, and Society (IDSS). “But there’s been a lot less research on where that organ comes from in the first place.”

      In the United States, nonprofits called organ procurement organizations, or OPOs, are responsible for finding and evaluating potential donors, interacting with grieving families and hospital administrations, and recovering and delivering organs — all while following the federal laws that serve as both their mandate and guardrails. Recent studies estimate that obstacles and inefficiencies lead to thousands of organs going uncollected every year, even as the demand for transplants continues to grow.

      “There’s been little transparent data on organ procurement,” argues Adam. Working with MIT computer science professors Marzyeh Ghassemi and Ashia Wilson, and in collaboration with stakeholders in organ procurement, Adam led a project to create a dataset called ORCHID: Organ Retrieval and Collection of Health Information for Donation. ORCHID contains a decade of clinical, financial, and administrative data from six OPOs.

      “Our goal is for the ORCHID database to have an impact in how organ procurement is understood, internally and externally,” says Ghassemi.

      Efficiency and equity 

      It was looking to make an impact that drew Adam to SES and MIT. With a background in applied math and experience in strategy consulting, solving problems with technical components sits right in his wheelhouse.

      “I really missed challenging technical problems from a statistics and machine learning standpoint,” he says of his time in consulting. “So I went back and got a master’s in data science, and over the course of my master’s got involved in a bunch of academic research projects in a few different fields, including biology, management science, and public policy. What I enjoyed most were some of the more social science-focused projects that had immediate impact.”

      As a grad student in SES, Adam’s research focuses on using statistical tools to uncover health-care inequities, and developing machine learning approaches to address them. “Part of my dissertation research focuses on building tools that can improve equity in clinical trials and other randomized experiments,” he explains.

      One recent example of Adam’s work: developing a novel method to stop clinical trials early if the treatment has an unintended harmful effect for a minority group of participants. “I’ve also been thinking about ways to increase minority representation in clinical trials through improved patient recruitment,” he adds.

      Racial inequities in health care extend into organ transplantation, where a majority of wait-listed patients are not white — far in excess of their demographic groups’ proportion to the overall population. There are fewer organ donations from many of these communities, due to various obstacles in need of better understanding if they are to be overcome. 

      “My work in organ transplantation began on the allocation side,” explains Adam. “In work under review, we examined the role of race in the acceptance of heart, liver, and lung transplant offers by physicians on behalf of their patients. We found that Black race of the patient was associated with significantly lower odds of organ offer acceptance — in other words, transplant doctors seemed more likely to turn down organs offered to Black patients. This trend may have multiple explanations, but it is nevertheless concerning.”

      Adam’s research has also found that donor-candidate race match was associated with significantly higher odds of offer acceptance, an association that Adam says “highlights the importance of organ donation from racial minority communities, and has motivated our work on equitable organ procurement.”

      Working with Ghassemi through the IDSS Initiative on Combatting Systemic Racism, Adam was introduced to OPO stakeholders looking to collaborate. “It’s this opportunity to impact not only health-care efficiency, but also health-care equity, that really got me interested in this research,” says Adam.

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      MIT Initiative on Combatting Systemic Racism – HealthcareVideo: IDSS

      Making an impact

      Creating a database like ORCHID means solving problems in multiple domains, from the technical to the political. Some efforts never overcome the first step: getting data in the first place. Thankfully, several OPOs were already seeking collaborations and looking to improve their performance.

      “We have been lucky to have a strong partnership with the OPOs, and we hope to work together to find important insights to improve efficiency and equity,” says Ghassemi.

      The value of a database like ORCHID is in its potential for generating new insights, especially through quantitative analysis with statistics and computing tools like machine learning. The potential value in ORCHID was recognized with an MIT Prize for Open Data, an MIT Libraries award highlighting the importance and impact of research data that is openly shared.

      “It’s nice that the work got some recognition,” says Adam of the prize. “And it was cool to see some of the other great open data work that’s happening at MIT. I think there’s real impact in releasing publicly available data in an important and understudied domain.”

      All the same, Adam knows that building the database is only the first step.

      “I’m very interested in understanding the bottlenecks in the organ procurement process,” he explains. “As part of my thesis research, I’m exploring this by modeling OPO decision-making using causal inference and structural econometrics.”

      Using insights from this research, Adam also aims to evaluate policy changes that can improve both equity and efficiency in organ procurement. “And we’re hoping to recruit more OPOs, and increase the amount of data we’re releasing,” he says. “The dream state is every OPO joins our collaboration and provides updated data every year.”

      Adam is excited to see how other researchers might use the data to address inefficiencies in organ procurement. “Every organ donor saves between three and four lives,” he says. “So every research project that comes out of this dataset could make a real impact.” More

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      Automated system teaches users when to collaborate with an AI assistant

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      Artificial intelligence models that pick out patterns in images can often do so better than human eyes — but not always. If a radiologist is using an AI model to help her determine whether a patient’s X-rays show signs of pneumonia, when should she trust the model’s advice and when should she ignore it?

      A customized onboarding process could help this radiologist answer that question, according to researchers at MIT and the MIT-IBM Watson AI Lab. They designed a system that teaches a user when to collaborate with an AI assistant.

      In this case, the training method might find situations where the radiologist trusts the model’s advice — except she shouldn’t because the model is wrong. The system automatically learns rules for how she should collaborate with the AI, and describes them with natural language.

      During onboarding, the radiologist practices collaborating with the AI using training exercises based on these rules, receiving feedback about her performance and the AI’s performance.

      The researchers found that this onboarding procedure led to about a 5 percent improvement in accuracy when humans and AI collaborated on an image prediction task. Their results also show that just telling the user when to trust the AI, without training, led to worse performance.

      Importantly, the researchers’ system is fully automated, so it learns to create the onboarding process based on data from the human and AI performing a specific task. It can also adapt to different tasks, so it can be scaled up and used in many situations where humans and AI models work together, such as in social media content moderation, writing, and programming.

      “So often, people are given these AI tools to use without any training to help them figure out when it is going to be helpful. That’s not what we do with nearly every other tool that people use — there is almost always some kind of tutorial that comes with it. But for AI, this seems to be missing. We are trying to tackle this problem from a methodological and behavioral perspective,” says Hussein Mozannar, a graduate student in the Social and Engineering Systems doctoral program within the Institute for Data, Systems, and Society (IDSS) and lead author of a paper about this training process.

      The researchers envision that such onboarding will be a crucial part of training for medical professionals.

      “One could imagine, for example, that doctors making treatment decisions with the help of AI will first have to do training similar to what we propose. We may need to rethink everything from continuing medical education to the way clinical trials are designed,” says senior author David Sontag, a professor of EECS, a member of the MIT-IBM Watson AI Lab and the MIT Jameel Clinic, and the leader of the Clinical Machine Learning Group of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

      Mozannar, who is also a researcher with the Clinical Machine Learning Group, is joined on the paper by Jimin J. Lee, an undergraduate in electrical engineering and computer science; Dennis Wei, a senior research scientist at IBM Research; and Prasanna Sattigeri and Subhro Das, research staff members at the MIT-IBM Watson AI Lab. The paper will be presented at the Conference on Neural Information Processing Systems.

      Training that evolves

      Existing onboarding methods for human-AI collaboration are often composed of training materials produced by human experts for specific use cases, making them difficult to scale up. Some related techniques rely on explanations, where the AI tells the user its confidence in each decision, but research has shown that explanations are rarely helpful, Mozannar says.

      “The AI model’s capabilities are constantly evolving, so the use cases where the human could potentially benefit from it are growing over time. At the same time, the user’s perception of the model continues changing. So, we need a training procedure that also evolves over time,” he adds.

      To accomplish this, their onboarding method is automatically learned from data. It is built from a dataset that contains many instances of a task, such as detecting the presence of a traffic light from a blurry image.

      The system’s first step is to collect data on the human and AI performing this task. In this case, the human would try to predict, with the help of AI, whether blurry images contain traffic lights.

      The system embeds these data points onto a latent space, which is a representation of data in which similar data points are closer together. It uses an algorithm to discover regions of this space where the human collaborates incorrectly with the AI. These regions capture instances where the human trusted the AI’s prediction but the prediction was wrong, and vice versa.

      Perhaps the human mistakenly trusts the AI when images show a highway at night.

      After discovering the regions, a second algorithm utilizes a large language model to describe each region as a rule, using natural language. The algorithm iteratively fine-tunes that rule by finding contrasting examples. It might describe this region as “ignore AI when it is a highway during the night.”

      These rules are used to build training exercises. The onboarding system shows an example to the human, in this case a blurry highway scene at night, as well as the AI’s prediction, and asks the user if the image shows traffic lights. The user can answer yes, no, or use the AI’s prediction.

      If the human is wrong, they are shown the correct answer and performance statistics for the human and AI on these instances of the task. The system does this for each region, and at the end of the training process, repeats the exercises the human got wrong.

      “After that, the human has learned something about these regions that we hope they will take away in the future to make more accurate predictions,” Mozannar says.

      Onboarding boosts accuracy

      The researchers tested this system with users on two tasks — detecting traffic lights in blurry images and answering multiple choice questions from many domains (such as biology, philosophy, computer science, etc.).

      They first showed users a card with information about the AI model, how it was trained, and a breakdown of its performance on broad categories. Users were split into five groups: Some were only shown the card, some went through the researchers’ onboarding procedure, some went through a baseline onboarding procedure, some went through the researchers’ onboarding procedure and were given recommendations of when they should or should not trust the AI, and others were only given the recommendations.

      Only the researchers’ onboarding procedure without recommendations improved users’ accuracy significantly, boosting their performance on the traffic light prediction task by about 5 percent without slowing them down. However, onboarding was not as effective for the question-answering task. The researchers believe this is because the AI model, ChatGPT, provided explanations with each answer that convey whether it should be trusted.

      But providing recommendations without onboarding had the opposite effect — users not only performed worse, they took more time to make predictions.

      “When you only give someone recommendations, it seems like they get confused and don’t know what to do. It derails their process. People also don’t like being told what to do, so that is a factor as well,” Mozannar says.

      Providing recommendations alone could harm the user if those recommendations are wrong, he adds. With onboarding, on the other hand, the biggest limitation is the amount of available data. If there aren’t enough data, the onboarding stage won’t be as effective, he says.

      In the future, he and his collaborators want to conduct larger studies to evaluate the short- and long-term effects of onboarding. They also want to leverage unlabeled data for the onboarding process, and find methods to effectively reduce the number of regions without omitting important examples.

      “People are adopting AI systems willy-nilly, and indeed AI offers great potential, but these AI agents still sometimes makes mistakes. Thus, it’s crucial for AI developers to devise methods that help humans know when it’s safe to rely on the AI’s suggestions,” says Dan Weld, professor emeritus at the Paul G. Allen School of Computer Science and Engineering at the University of Washington, who was not involved with this research. “Mozannar et al. have created an innovative method for identifying situations where the AI is trustworthy, and (importantly) to describe them to people in a way that leads to better human-AI team interactions.”

      This work is funded, in part, by the MIT-IBM Watson AI Lab. More

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      Meet the 2023-24 Accenture Fellows

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      The MIT and Accenture Convergence Initiative for Industry and Technology has selected five new research fellows for 2023-24. Now in its third year, the initiative underscores the ways in which industry and research can collaborate to spur technological innovation.

      Through its partnership with the School of Engineering, Accenture provides five annual fellowships awarded to graduate students with the aim of generating powerful new insights on the convergence of business and technology with the potential to transform society. The 2023-24 fellows will conduct research in areas including artificial intelligence, sustainability, and robotics.

      The 2023-24 Accenture Fellows are:

      Yiyue Luo

      Yiyue Luo is a PhD candidate who is developing innovative integrations of tactile sensing and haptics, interactive sensing and AI, digital fabrication, and smart wearables. Her work takes advantage of recent advances in digital manufacturing and AI, and the convergence in advanced sensing and actuation mechanisms, scalable digital manufacturing, and emerging computational techniques, with the goal of creating novel sensing and actuation devices that revolutionize interactions between people and their environments. In past projects, Luo has developed tactile sensing apparel including socks, gloves, and vests, as well as a workflow for computationally designing and digitally fabricating soft textiles-based pneumatic actuators. With the support of an Accenture Fellowship, she will advance her work of combining sensing and actuating devices and explore the development of haptic devices that simulate tactile cues captured by tactile sensors. Her ultimate aim is to build a scalable, textile-based, closed-loop human-machine interface. Luo’s research holds exciting potential to advance ground-breaking applications for smart textiles, health care, artificial and virtual reality, human-machine interactions, and robotics.

      Zanele Munyikwa is a PhD candidate whose research explores foundation models, a class of models that forms the basis of transformative general-purpose technologies (GPTs) such as GPT4. An Accenture Fellowship will enable Munyikwa to conduct research aimed at illuminating the current and potential impact of foundation models (including large language models) on work and tasks common to “high-skilled” knowledge workers in industries such as marketing, legal services, and medicine, in which foundation models are expected to have significant economic and social impacts. A primary goal of her project is to observe the impact of AI augmentation on tasks like copywriting and long-form writing. A second aim is to explore two primary ways that foundation models are driving the convergence of creative and technological industries, namely: reducing the cost of content generation and enabling the development of tools and platforms for education and training. Munyikwa’s work has important implications for the use of foundation models in many fields, from health care and education to legal services, business, and technology.

      Michelle Vaccaro is a PhD candidate in social engineering systems whose research explores human-AI collaboration with the goals of developing a deeper understanding of AI-based technologies (including ChatGPT and DALL-E), evaluating their performance and evolution, and steering their development toward societally beneficial applications, like climate change mitigation. An Accenture Fellowship will support Vaccaro’s current work toward two key objectives: identifying synergies between humans and AI-based software to help design human-AI systems that address persistent problems better than existing approaches; and investigating applications of human-AI collaboration for forecasting technological change, specifically for renewable energy technologies. By integrating the historically distinct domains of AI, systems engineering, and cognitive science with a wide range of industries, technical fields, and social applications, Vaccaro’s work has the potential to advance individual and collective productivity and creativity in all these areas.

      Chonghuan Wang is a PhD candidate in computational science and engineering whose research employs statistical learning, econometrics theory, and experimental design to create efficient, reliable, and sustainable field experiments in various domains. In his current work, Wang is applying statistical learning techniques such as online learning and bandit theory to test the effectiveness of new treatments, vaccinations, and health care interventions. With the support of an Accenture Fellowship, he will design experiments with the specific aim of understanding the trade-off between the loss of a patient’s welfare and the accuracy of estimating the treatment effect. The results of this research could help to save lives and contain disease outbreaks during pandemics like Covid-19. The benefits of enhanced experiment design and the collection of high-quality data extend well beyond health care; for example, these tools could help businesses optimize user engagement, test pricing impacts, and increase the usage of platforms and services. Wang’s research holds exciting potential to harness statistical learning, econometrics theory, and experimental design in support of strong businesses and the greater social good.

      Aaron Michael West Jr. is a PhD candidate whose research seeks to enhance our knowledge of human motor control and robotics. His work aims to advance rehabilitation technologies and prosthetic devices, as well as improve robot dexterity. His previous work has yielded valuable insights into the human ability to extract information solely from visual displays. Specifically, he demonstrated humans’ ability to estimate stiffness based solely on the visual observation of motion. These insights could advance the development of software applications with the same capability (e.g., using machine learning methods applied to video data) and may enable roboticists to develop enhanced motion control such that a robot’s intention is perceivable by humans. An Accenture Fellowship will enable West to continue this work, as well as new investigations into the functionality of the human hand to aid in the design of a prosthetic hand that better replicates human dexterity. By advancing understandings of human bio- and neuro-mechanics, West’s work has the potential to support major advances in robotics and rehabilitation technologies, with profound impacts on human health and well-being. More

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      M’Care and MIT students join forces to improve child health in Nigeria

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      Through a collaboration between M’Care, a 2021 Health Security and Pandemics Solver team, and students from MIT, the landscape of child health care in Nigeria could undergo a transformative change, wherein the power of data is harnessed to improve child health outcomes in economically disadvantaged communities. 

      M’Care is a mobile application of Promane and Promade Limited, developed by Opeoluwa Ashimi, which gives community health workers in Nigeria real-time diagnostic and treatment support. The application also creates a dashboard that is available to government health officials to help identify disease trends and deploy timely interventions. As part of its work, M’Care is working to mitigate malnutrition by providing micronutrient powder, vitamin A, and zinc to children below the age of 5. To help deepen its impact, Ashimi decided to work with students in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) course 6.S897 (Machine Learning for Healthcare) — instructed by professors Peter Szolovits and Manolis Kellis — to leverage data in order to improve nutrient delivery to children across Nigeria. The collaboration also enabled students to see real-world applications for data analysis in the health care space.

      A meeting of minds: M’Care, MIT, and national health authorities

      “Our primary goal for collaborating with the ML for Health team was to spot the missing link in the continuum of care. With over 1 million cumulative consultations that qualify for a continuum of care evaluation, it was important to spot why patients could be lost to followup, prevent this, and ensure completion of care to successfully address the health needs of our patients,” says Ashimi, founder and CEO of M’Care.

      In May 2023, Ashimi attended a meeting that brought together key national stakeholders, including the representatives of the National Ministry of Health in Nigeria. This gathering served as a platform to discuss the profound impact of M’Care’s and ML for Health team’s collaboration — bolstered by data analysis provided on dosage regimens and a child’s age to enhance continuum of care with its attendant impact on children’s health, particularly in relation to brain development with regards to the use of essential micronutrients. The data analyzed by the students using ML methods that were shared during the meeting provided strong supporting evidence to individualize dosage regimens for children based on their age in months for the ANRIN project — a national nutrition project supported by the World Bank — as well as policy decisions to extend months of coverage for children, redefining health care practices in Nigeria.

      MIT students drive change by harnessing the power of data

      At the heart of this collaboration lies the contribution of MIT students. Armed with their dedication and skill in data analysis and machine learning, they played a pivotal role in helping M’Care analyze their data and prepare for their meeting with the Ministry of Health. Their most significant findings included ways to identify patients at risk of not completing their full course of micronutrient powder and/or vitamin A, and identifying gaps in M’Care’s data, such as postdated delivery dates and community demographics. These findings are already helping M’Care better plan its resources and adjust the scope of its program to ensure more children complete the intervention.

      Darcy Kim, an undergraduate at Wellesley College studying math and computer science, who is cross-registered for the MIT machine learning course, expresses enthusiasm about the practical applications found within the project: “To me, data and math is storytelling, and the story is why I love studying it. … I learned that data exploration involves asking questions about how the data is collected, and that surprising patterns that arise often have a qualitative explanation. Impactful research requires radical collaboration with the people the research intends to help. Otherwise, these qualitative explanations get lost in the numbers.”

      Joyce Luo, a first-year operations research PhD student at the Operations Research Center at MIT, shares similar thoughts about the project: “I learned the importance of understanding the context behind data to figure out what kind of analysis might be most impactful. This involves being in frequent contact with the company or organization who provides the data to learn as much as you can about how the data was collected and the people the analysis could help. Stepping back and looking at the bigger picture, rather than just focusing on accuracy or metrics, is extremely important.”

      Insights to implementation: A new era for micronutrient dosing

      As a direct result of M’Care’s collaboration with MIT, policymakers revamped the dosing scheme for essential micronutrient administration for children in Nigeria to prevent malnutrition. M’Care and MIT’s data analysis unearthed critical insights into the limited frequency of medical visits caused by late-age enrollment. 

      “One big takeaway for me was that the data analysis portion of the project — doing a deep dive into the data; understanding, analyzing, visualizing, and summarizing the data — can be just as important as building the machine learning models. M’Care shared our data analysis with the National Ministry of Health, and the insights from it drove them to change their dosing scheme and schedule for delivering micronutrient powder to young children. This really showed us the value of understanding and knowing your data before modeling,” shares Angela Lin, a second-year PhD student at the Operations Research Center.

      Armed with this knowledge, policymakers are eager to develop an optimized dosing scheme that caters to the unique needs of children in disadvantaged communities, ensuring maximum impact on their brain development and overall well-being.

      Siddharth Srivastava, M’Care’s corporate technology liaison, shares his gratitude for the MIT student’s input. “Collaborating with enthusiastic and driven students was both empowering and inspiring. Each of them brought unique perspectives and technical skills to the table. Their passion for applying machine learning to health care was evident in their unwavering dedication and proactive approach to problem-solving.”

      Forging a path to impact

      The collaboration between M’Care and MIT exemplifies the remarkable achievements that arise when academia, innovative problem-solvers, and policy authorities unite. By merging academic rigor with real-world expertise, this partnership has the potential to revolutionize child health care not only in Nigeria but also in similar contexts worldwide.

      “I believe applying innovative methods of machine learning, data gathering, instrumentation, and planning to real problems in the developing world can be highly effective for those countries and highly motivating for our students. I was happy to have such a project in our class portfolio this year and look forward to future opportunities,” says Peter Szolovits, professor of computer science and engineering at MIT.

      By harnessing the power of data, innovation, and collective expertise, this collaboration between M’Care and MIT has the potential to improve equitable child health care in Nigeria. “It has been so fulfilling to see how our team’s work has been able to create even the smallest positive impact in such a short period of time, and it has been amazing to work with a company like Promane and Promade Limited that is so knowledgeable and caring for the communities that they serve,” shares Elizabeth Whittier, a second-year PhD electrical engineering student at MIT. More

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      How machine learning models can amplify inequities in medical diagnosis and treatment

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      Prior to receiving a PhD in computer science from MIT in 2017, Marzyeh Ghassemi had already begun to wonder whether the use of AI techniques might enhance the biases that already existed in health care. She was one of the early researchers to take up this issue, and she’s been exploring it ever since. In a new paper, Ghassemi, now an assistant professor in MIT’s Department of Electrical Science and Engineering (EECS), and three collaborators based at the Computer Science and Artificial Intelligence Laboratory, have probed the roots of the disparities that can arise in machine learning, often causing models that perform well overall to falter when it comes to subgroups for which relatively few data have been collected and utilized in the training process. The paper — written by two MIT PhD students, Yuzhe Yang and Haoran Zhang, EECS computer scientist Dina Katabi (the Thuan and Nicole Pham Professor), and Ghassemi — was presented last month at the 40th International Conference on Machine Learning in Honolulu, Hawaii.

      In their analysis, the researchers focused on “subpopulation shifts” — differences in the way machine learning models perform for one subgroup as compared to another. “We want the models to be fair and work equally well for all groups, but instead we consistently observe the presence of shifts among different groups that can lead to inferior medical diagnosis and treatment,” says Yang, who along with Zhang are the two lead authors on the paper. The main point of their inquiry is to determine the kinds of subpopulation shifts that can occur and to uncover the mechanisms behind them so that, ultimately, more equitable models can be developed.

      The new paper “significantly advances our understanding” of the subpopulation shift phenomenon, claims Stanford University computer scientist Sanmi Koyejo. “This research contributes valuable insights for future advancements in machine learning models’ performance on underrepresented subgroups.”

      Camels and cattle

      The MIT group has identified four principal types of shifts — spurious correlations, attribute imbalance, class imbalance, and attribute generalization — which, according to Yang, “have never been put together into a coherent and unified framework. We’ve come up with a single equation that shows you where biases can come from.”

      Biases can, in fact, stem from what the researchers call the class, or from the attribute, or both. To pick a simple example, suppose the task assigned to the machine learning model is to sort images of objects — animals in this case — into two classes: cows and camels. Attributes are descriptors that don’t specifically relate to the class itself. It might turn out, for instance, that all the images used in the analysis show cows standing on grass and camels on sand — grass and sand serving as the attributes here. Given the data available to it, the machine could reach an erroneous conclusion — namely that cows can only be found on grass, not on sand, with the opposite being true for camels. Such a finding would be incorrect, however, giving rise to a spurious correlation, which, Yang explains, is a “special case” among subpopulation shifts — “one in which you have a bias in both the class and the attribute.”

      In a medical setting, one could rely on machine learning models to determine whether a person has pneumonia or not based on an examination of X-ray images. There would be two classes in this situation, one consisting of people who have the lung ailment, another for those who are infection-free. A relatively straightforward case would involve just two attributes: the people getting X-rayed are either female or male. If, in this particular dataset, there were 100 males diagnosed with pneumonia for every one female diagnosed with pneumonia, that could lead to an attribute imbalance, and the model would likely do a better job of correctly detecting pneumonia for a man than for a woman. Similarly, having 1,000 times more healthy (pneumonia-free) subjects than sick ones would lead to a class imbalance, with the model biased toward healthy cases. Attribute generalization is the last shift highlighted in the new study. If your sample contained 100 male patients with pneumonia and zero female subjects with the same illness, you still would like the model to be able to generalize and make predictions about female subjects even though there are no samples in the training data for females with pneumonia.

      The team then took 20 advanced algorithms, designed to carry out classification tasks, and tested them on a dozen datasets to see how they performed across different population groups. They reached some unexpected conclusions: By improving the “classifier,” which is the last layer of the neural network, they were able to reduce the occurrence of spurious correlations and class imbalance, but the other shifts were unaffected. Improvements to the “encoder,” one of the uppermost layers in the neural network, could reduce the problem of attribute imbalance. “However, no matter what we did to the encoder or classifier, we did not see any improvements in terms of attribute generalization,” Yang says, “and we don’t yet know how to address that.”

      Precisely accurate

      There is also the question of assessing how well your model actually works in terms of evenhandedness among different population groups. The metric normally used, called worst-group accuracy or WGA, is based on the assumption that if you can improve the accuracy — of, say, medical diagnosis — for the group that has the worst model performance, you would have improved the model as a whole. “The WGA is considered the gold standard in subpopulation evaluation,” the authors contend, but they made a surprising discovery: boosting worst-group accuracy results in a decrease in what they call “worst-case precision.” In medical decision-making of all sorts, one needs both accuracy — which speaks to the validity of the findings — and precision, which relates to the reliability of the methodology. “Precision and accuracy are both very important metrics in classification tasks, and that is especially true in medical diagnostics,” Yang explains. “You should never trade precision for accuracy. You always need to balance the two.”

      The MIT scientists are putting their theories into practice. In a study they’re conducting with a medical center, they’re looking at public datasets for tens of thousands of patients and hundreds of thousands of chest X-rays, trying to see whether it’s possible for machine learning models to work in an unbiased manner for all populations. That’s still far from the case, even though more awareness has been drawn to this problem, Yang says. “We are finding many disparities across different ages, gender, ethnicity, and intersectional groups.”

      He and his colleagues agree on the eventual goal, which is to achieve fairness in health care among all populations. But before we can reach that point, they maintain, we still need a better understanding of the sources of unfairness and how they permeate our current system. Reforming the system as a whole will not be easy, they acknowledge. In fact, the title of the paper they introduced at the Honolulu conference, “Change is Hard,” gives some indications as to the challenges that they and like-minded researchers face. More

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      Novo Nordisk to support MIT postdocs working at the intersection of AI and life sciences

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      MIT’s School of Engineering and global health care company Novo Nordisk has announced the launch of a multi-year program to support postdoctoral fellows conducting research at the intersection of artificial intelligence and data science with life sciences. The MIT-Novo Nordisk Artificial Intelligence Postdoctoral Fellows Program will welcome its first cohort of up to 10 postdocs for a two-year term this fall. The program will provide up to $10 million for an annual cohort of up to 10 postdoc for two-year terms.

      “The research being conducted at the intersection of AI and life sciences has the potential to transform health care as we know it,” says Anantha Chandrakasan, dean of the School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science. “I am thrilled that the MIT-Novo Nordisk Program will support early-career researchers who work in this space.”

      The launch of the MIT-Novo Nordisk Program coincides with the 100th anniversary celebration of Novo Nordisk. The company was founded in 1923 and treated its first patients with insulin, which had recently been discovered in March of that year.

      “The use of AI in the health care industry presents a massive opportunity to improve the lives of people living with chronic diseases,” says Thomas Senderovitz, senior vice president for data science at Novo Nordisk. “Novo Nordisk is committed to the development of new, innovative solutions, and MIT hosts some of the most outstanding researchers in the field. We are therefore excited to support postdocs working on the cutting edge of AI and life sciences.”

      The MIT-Novo Nordisk Program will support postdocs advancing the use of AI in life science and health. Postdocs will join an annual cohort that participates in frequent events and gatherings. The cohort will meet regularly to exchange ideas about their work and discuss ways to amplify their impact.

      “We are excited to welcome postdocs working on AI, data science, health, and life sciences — research areas of strategic importance across MIT,” adds Chandrakasan.

      A central focus of the program will be offering postdocs professional development and mentorship opportunities. Fellows will be invited to entrepreneurship-focused workshops that enable them to learn from company founders, venture capitalists, and other entrepreneurial leaders. Fellows will also have the opportunity to receive mentorship from experts in life sciences and data science.

      “MIT is always exploring opportunities to innovate and enhance the postdoctoral experience,” adds MIT Provost Cynthia Barnhart. “The MIT-Novo Nordisk Program has been thoughtfully designed to introduce fellows to a wealth of experiences, skill sets, and perspectives that support their professional growth while prioritizing a sense of community with their cohort.”

      Angela Belcher, head of the Department of Biological Engineering, the James Mason Crafts Professor of Biological Engineering and Materials Science, and member of the Koch Institute for Integrative Cancer Research, and Asu Ozdaglar, deputy dean of academics for the MIT Schwarzman College of Computing and head of the Department of Electrical Engineering and Computer Science, will serve as co-faculty leads for the program.

      The new program complements a separate postdoctoral fellowship program at MIT supported by the Novo Nordisk Foundation that focuses on enabling interdisciplinary research. More