Aurelia Butler

In the United States and other western countries, a form of esophageal and stomach cancer has risen dramatically over the last five decades. Rates of esophageal adenocarcinoma, or EAC, and gastric cardia adenocarcinoma, or GCA, are both highly fatal.
However, Joel Rubenstein, M.D., M.S., a research scientist at the Lieutenant Colonel Charles S. Kettles Veterans Affairs Center for Clinical Management Research and professor of internal medicine at Michigan Medicine, says that preventative measures can be a saving grace.
“Screening can identify pre-cancerous changes in patients, Barrett’s esophagus, which is sometimes diagnosed in individuals who have long-term gastroesophageal reflux disease, or GERD,” he said.
“When early detection occurs, patients can take additional steps to help prevent cancer.”
While current guidelines already consider screening in high-risk patients, Rubenstein notes that many providers are still unfamiliar with this recommendation.
“Many individuals who develop these types of cancer never had screening to begin with,” he said.
“But a new automated tool embedded in the electronic health record holds the potential to bridge the gap between provider awareness and patients who are at an increased risk of developing esophageal adenocarcinoma and gastric cardia adenocarcinoma.”
Rubenstein and a team of researchers used a type of artificial intelligence to examine data regarding EAC and GCA rates in over 10 million U.S. veterans. More

How is a donut similar to a coffee cup? This question often serves as an illustrative example to explain the concept of topology. Topology is a field of mathematics that examines the properties of objects that remain consistent even when they are stretched or deformed — provided they are not torn or stitched together. For instance, both a donut and a coffee cup have a single hole. This means, theoretically, if either were pliable enough, it could be reshaped into the other. This branch of mathematics provides a more flexible way to describe shapes in data, such as the connections between individuals in a social network or the atomic coordinates of materials. This understanding has led to the development of a novel technique: topological data analysis.
In a study published this month in The Journal of Chemical Physics, researchers from SANKEN (The Institute of Scientific and Industrial Research) at Osaka University and two other universities have used topological data analysis and machine learning to formulate a new method to predict the properties of amorphous materials.
A standout technique in the realm of topological data analysis is persistent homology. This method offers insights into topological features, specifically the “holes” and “cavities” within data. When applied to material structures, it allows us to identify and quantify their crucial structural characteristics.
Now, these researchers have employed a method that combines persistent homology and machine learning to predict the properties of amorphous materials. Amorphous materials, which include substances like glass, consist of disordered particles that lack repeating patterns.
A crucial aspect of using machine-learning models to predict the physical properties of amorphous substances lies in finding an appropriate method to convert atomic coordinates into a list of vectors. Merely utilizing coordinates as a list of vectors is inadequate because the energies of amorphous systems remain unchanged with rotation, translation, and permutation of the same type of atoms. Consequently, the representation of atomic configurations should embody these symmetry constraints. Topological methods are inherently well-suited for such challenges. “Using conventional methods to extract information about the connections between numerous atoms characterizing amorphous structures was challenging. However, the task has become more straightforward with the application of persistent homology,” explains Emi Minamitani, the lead author of the study.
The researchers discovered that by integrating persistent homology with basic machine-learning models, they could accurately predict the energies of disordered structures composed of carbon atoms at varying densities. This strategy demands significantly less computational time compared to quantum mechanics-based simulations of these amorphous materials.
The techniques showcased in this study hold potential for facilitating more efficient and rapid calculations of material properties in other disordered systems, such as amorphous glasses or metal alloys. More

Proteins consist of amino acids, which are linked to form long amino acid chains as specified by our genetic material. In our cells, these chains are not simply rolled up like strings of pearls, but fold into complex, three-dimensional structures. How a protein is folded decisively influences its function: It determines, for example, which other molecules a protein can interact with in the cell. Knowledge of the three-dimensional structure of proteins is therefore of great interest to the life sciences and plays a role in drug development, among other things.
“Unfortunately, elucidating the structure of a protein is anything but trivial, and focusing on a single state does not always provide meaningful information, especially if the protein is highly flexible in terms of its structure,” says Tobias Schneider, a member of Michael Kovermann’s lab team in the Department of Chemistry at the University of Konstanz. The reason: complex proteins often fold into several compact subunits, called domains, which in turn may be connected by flexible linkers. The more flexibly connected subunits are present, the more different three-dimensional structures a protein can theoretically adopt. “This means that a protein in solution, for example inside our cells, has several equal states and constantly switches between them,” Schneider explains.
Tracking down the structural ensemble
A simple snapshot is not sufficient to fully describe the structural features of such multi-domain proteins, as it would capture only one of many states at a time. To get a detailed picture of the possible structures of such proteins, a smart combination of different methods is needed. In an article published in the journal Structure, Konstanz biophysicists led by Michael Kovermann and Christine Peter (also Department of Chemistry) present a corresponding approach using complementary methods.
“Through NMR spectroscopy, for example, we get information about the dynamic properties of such proteins. Complex computer simulations, on the other hand, provide a good overview of the range of possible folds,” explains Kovermann. “So far, no general approach that comprehensively maps the dynamic and structural properties of multi-domain proteins had existed.” The researchers from Konstanz therefore devised a workflow that combines NMR spectroscopy and computer simulations, allowing them to obtain information on both properties with high temporal and spatial resolution.
Proof of feasibility included
The researchers also provided evidence that the method works: They examined various ubiquitin dimers. These consist of two units of the protein ubiquitin that are linked by a flexible bond, just like the situation in cells. It is thus a prime example of a multi-domain protein for which different structural models have been suggested so far and which is of great scientific interest.
The researchers were able to show that the ubiquitin dimers they studied exhibit a high structural variability and that this can be described in detail using the developed combination of methods. The results also explain the different structural models that currently exist of ubiquitin dimers. “We are convinced that our approach — combining complementary methods — will work not only for ubiquitin dimers but also for other multi-domain proteins,” Schneider says. “Our study opens new avenues to better understand the high structural diversity of these complex proteins that plays a crucial role in their biological functions.” More

Structural formulae show how chemical compounds are constructed, i.e., which atoms they consist of, how these are arranged spatially and how they are connected. Chemists can deduce from a structural formula, among other things, which molecules can react with each other and which cannot, how complex compounds can be synthesised or which natural substances could have a therapeutic effect because they fit together with target molecules in cells.
Developed in the 19th century, the representation of molecules as structural formulae has stood the test of time and is still used in every chemistry textbook. But what makes the chemical world intuitively comprehensible for humans is just a collection of black and white pixels for software. “To make the information from structural formulae usable in databases that can be searched automatically, they have to be translated into a machine-readable code,” explains Christoph Steinbeck, Professor for Analytical Chemistry, Cheminformatics and Chemometrics at the University of Jena.
An image becomes a code
And that is precisely what can be done using the Artificial Intelligence tool “DECIMER,” developed by the team led by Prof. Steinbeck and his colleague Prof. Achim Zielesny from the Westphalian University of Applied Sciences. DECIMER stands for “Deep Learning for Chemical Image Recognition.” It is an open-source platform that is freely available to everyone on the Internet and can be used in a standard web browser. Scientific articles containing chemical structural formulae can be uploaded there simply by dragging and dropping, and the AI tool will immediately get to work.
“First, the entire document is searched for images,” explains Steinbeck. The algorithm then identifies the image information contained and classifies it according to whether it is a chemical structural formula or some other image. Finally, the structural formulae recognised are translated into the chemical structure code or displayed in a structure editor, so that they can be further processed. “This step is the core of the project and the real achievement,” adds Steinbeck.
In this way, the chemical structural formula for the caffeine molecule becomes the machine-readable structure code CN1C=NC2=C1C(=O)N(C(=O)N2C)C. This can then be uploaded directly into a database and linked to further information on the molecule.
To develop DECIMER, the researchers used modern AI methods that have only recently become established and are also used, for example, in the Large Language Models (such as ChatGPT) that are currently the subject of much discussion. To train its AI tool, the team generated structural formulas from the existing machine-readable databases and used them as training data — some 450 million structural formulas to date. In addition to researchers, companies are also already using the AI tool, for example to transfer structural formulae from patent specifications into databases. More

A new study led by investigators from Mass General Brigham has found that ChatGPT was about 72 percent accurate in overall clinical decision making, from coming up with possible diagnoses to making final diagnoses and care management decisions. The large-language model (LLM) artificial intelligence chatbot performed equally well in both primary care and emergency settings across all medical specialties. The research team’s results are published in the Journal of Medical Internet Research.
“Our paper comprehensively assesses decision support via ChatGPT from the very beginning of working with a patient through the entire care scenario, from differential diagnosis all the way through testing, diagnosis, and management,” said corresponding author Marc Succi, MD, associate chair of innovation and commercialization and strategic innovation leader at Mass General Brigham and executive director of the MESH Incubator. “No real benchmarks exists, but we estimate this performance to be at the level of someone who has just graduated from medical school, such as an intern or resident. This tells us that LLMs in general have the potential to be an augmenting tool for the practice of medicine and support clinical decision making with impressive accuracy.”
Changes in artificial intelligence technology are occurring at a fast pace and transforming many industries, including health care. But the capacity of LLMs to assist in the full scope of clinical care has not yet been studied. In this comprehensive, cross-specialty study of how LLMs could be used in clinical advisement and decision making, Succi and his team tested the hypothesis that ChatGPT would be able to work through an entire clinical encounter with a patient and recommend a diagnostic workup, decide the clinical management course, and ultimately make the final diagnosis.
The study was done by pasting successive portions of 36 standardized, published clinical vignettes into ChatGPT. The tool first was asked to come up with a set of possible, or differential, diagnoses based on the patient’s initial information, which included age, gender, symptoms, and whether the case was an emergency. ChatGPT was then given additional pieces of information and asked to make management decisions as well as give a final diagnosis — simulating the entire process of seeing a real patient. The team compared ChatGPT’s accuracy on differential diagnosis, diagnostic testing, final diagnosis, and management in a structured blinded process, awarding points for correct answers and using linear regressions to assess the relationship between ChatGPT’s performance and the vignette’s demographic information.
The researchers found that overall, ChatGPT was about 72 percent accurate and that it was best in making a final diagnosis, where it was 77 percent accurate. It was lowest-performing in making differential diagnoses, where it was only 60 percent accurate. And it was only 68 percent accurate in clinical management decisions, such as figuring out what medications to treat the patient with after arriving at the correct diagnosis. Other notable findings from the study included that ChatGPT’s answers did not show gender bias and that its overall performance was steady across both primary and emergency care.
“ChatGPT struggled with differential diagnosis, which is the meat and potatoes of medicine when a physician has to figure out what to do,” said Succi. “That is important because it tells us where physicians are truly experts and adding the most value — in the early stages of patient care with little presenting information, when a list of possible diagnoses is needed.”
The authors note that before tools like ChatGPT can be considered for integration into clinical care, more benchmark research and regulatory guidance is needed. Next, Succi’s team is looking at whether AI tools can improve patient care and outcomes in hospitals’ resource-constrained areas.
The emergence of artificial intelligence tools in health has been groundbreaking and has the potential to positively reshape the continuum of care. Mass General Brigham, as one of the nation’s top integrated academic health systems and largest innovation enterprises, is leading the way in conducting rigorous research on new and emerging technologies to inform the responsible incorporation of AI into care delivery, workforce support, and administrative processes.
“Mass General Brigham sees great promise for LLMs to help improve care delivery and clinician experience,” said co-author Adam Landman, MD, MS, MIS, MHS, chief information officer and senior vice president of digital at Mass General Brigham. “We are currently evaluating LLM solutions that assist with clinical documentation and draft responses to patient messages with focus on understanding their accuracy, reliability, safety, and equity. Rigorous studies like this one are needed before we integrate LLM tools into clinical care.” More

Social risk factors such as financial instability and housing insecurity are increasingly recognized as influencing health. But unlike diagnosis codes, prescription information, lab or other test reports, social risk factors do not adhere to standardized, controlled terminology in a patient’s electronic medical record, making this information difficult to extract from the clinical notes where they typically are found.
A new study has found that a natural language processing (NLP) system developed by Regenstrief Institute and Indiana University Richard M. Fairbanks School of Public Health informaticians showed excellent performance when ported to a new health system and tested on more than six million clinical notes of patients seen in Florida. Performance was evaluated for generalizability and portability, defined as ease and accuracy when deploying the software in a new environment and of updating its use to meet the needs of new data.
“Social factors have a great impact on our health. It’s not just the medical care that we receive, but it’s also the places where we live, the places where we work and our access to food and transportation and other resources that have a major influence on our health,” said Chris Harle, PhD, the Regenstrief and IU Fairbanks School faculty member who is senior author on the study. “It’s important for the clinicians and health systems providing medical care to know about people’s social risk factors so when prescribing medications, ordering tests or planning to perform a procedure, they can better treat the whole person — perhaps with lower cost drugs or alternative sources for tests — and can also link them to services that help address their needs for a safe place to live and healthy food to eat.”
In this study, the researchers’ NLP rule-based model searched through text that physicians or other clinicians had written in the clinical notes of patients’ electronic health records, looking for key words or phrases that were likely to indicate difficulty with housing (for example: lack of permanent address) or financial needs (for example: inability to afford follow-up care) of patients at a healthcare system in a new and quite different geographic area. In spite of challenges (for example: name of a homeless shelter without indication of the facility’s function or regional variation or local nuances in language), the research scientists verified that the NLP models, with relatively simple modifications, could deliver highly accurate performance as compared to the gold standard of human review.
“Is a patient diagnosed with diabetes? It’s relatively easy to find that information in an electronic health record because the same words and codes are more likely to be used in health systems in central Indiana as are used in Florida or elsewhere in the U.S. But social risk factors don’t have nearly as established and widely used words, phrases or codes to identify them. Therefore, it’s harder to search through and determine a patient has a financial need than it is to say a patient has diabetes,” said Dr. Harle. “Our work is important for patients because ultimately their health is related to a variety of factors in their life, including social factors. For example, are clinicians incorporating in their decision making a patient’s ability to recover from a surgery as it’s going to be different if they have stable housing versus unstable housing?
“The more that we can disseminate and adapt natural language processing and other artificial intelligence methods that fully describe a patient to give clinicians a full 360 understanding of patients’ needs, the better. If we can extract social information more efficiently, it’s less costly. Then we can start to take what we’d call a population health perspective. So, if a health system can efficiently identify the patients who have housing instability — the population of patients who have this need — then the healthcare system may be able to employ a more proactive population-based intervention to serve that whole group of people, connecting them, for example, to the housing services in the community or financial resources that might be available.”
Dr. Harle, an information scientist and health services researcher who focuses on the design, adoption, use and value of health information systems, notes that this study was a team effort across multiple institutions of professionals who work in the clinical arena (including individuals who study how patients access and use care), public health, population health and healthcare administration as well as technically knowledgeable and skilled systems specialists. “Bringing people together who have that diversity of understanding leads to pragmatically useful studies like this one,” he said. More

As children head back to school, two issues have climbed higher on their parents’ list of concerns: the role of social media and the internet in kids’ lives.
Over half of parents also rate mental health issues as leading health concerns for children and teens, according to the University of Michigan Health C.S. Mott Children’s Hospital National Poll on Children’s Health.
Overall, emotional health and technology use dominated this year’s top 10 list of parent concerns about health-related issues for kids in the U.S.- surpassing childhood obesity, which parents rated the number one children’s health issue a decade ago.
“Parents still view problems directly impacting physical health, including unhealthy eating and obesity, as important children’s health issues. But these have been overtaken by concerns about mental health, social media and screen time,” said Mott Poll co-director and Mott pediatrician Susan Woolford, M.D., M.P.H.
Two-thirds of parents are worried about children’s increased time on devices, including overall screen time and use of social media, taking the No.1 and No.2 spots on the list of children’s health concerns this year, according to the nationally representative poll.
“Children are using digital devices and social media at younger ages, and parents may struggle with how to appropriately monitor use to prevent negative impacts on safety, self-esteem, social connections and habits that may interfere with sleep and other areas of health,” Woolford said.
Screen time became a growing concern for parents during the pandemic, previous reports suggest. Woolford encourages parents to regularly evaluate their children’s use of technology and consider limiting use if they notice signs of unhealthy interactions or behaviors. Certain social media and device settings can also help protect kids. More