MEDICAL EXPRESS - HEALTH INFORMATICS

1. Social chatbots can effectively alleviate loneliness and social anxiety

   A recent study has found that social chatbots, an interactive form of artificial intelligence (AI), are effective in alleviating feelings of loneliness and social anxiety in university students. The study is published in the Journal of Medical Internet Research.

2. It's inoperable cancer. Should AI make call about what happens next?

   AI is already being used in clinics to help analyze imaging data, such as X-rays and scans. But the recent arrival of sophisticated large-language AI models on the scene is forcing consideration of broadening the use of the technology into other areas of patient care.

3. Effect of behavioral intervention on reducing care overuse in seniors not sustained, study reveals

   Clinical decision support (CDS) aimed at reducing overuse of care in seniors does not yield durable changes in terms of prostate-specific antigen (PSA) screening in older men, but is durable for reducing urinary overtesting, according to a research letter published online Feb. 11 in the Annals of Internal Medicine.

4. Brain-inspired neural networks reveal insights into biological basis of relational learning

   Humans and certain animals appear to have an innate capacity to learn relationships between different objects or events in the world. This ability, known as "relational learning," is widely regarded as critical for cognition and intelligence, as learned relationships are thought to allow humans and animals to navigate new situations.

5. Deep learning could increase the accessibility and ease of heart imaging

   Coronary artery disease is the leading cause of death globally. One of the most common tools used to diagnose and monitor heart disease, myocardial perfusion imaging (MPI) by single photon emission computed tomography (SPECT), uses a radioactive tracer and special camera to provide detailed images of blood flow to the heart, helping doctors detect coronary artery disease and other cardiovascular abnormalities. However, traditional SPECT imaging requires an additional CT scan to ensure accurate results, exposing patients to more radiation and increasing costs.

6. Advanced imaging and AI reveal smoking-related toxins in placenta samples

   Rice University scientists and collaborators at Baylor College of Medicine (BCM) have demonstrated a new method for detecting the presence of dangerous chemicals from tobacco smoke in human placenta with unprecedented speed and precision.

7. AI proves better than humans at analyzing long-term ECG recordings in large international study

   In patients with symptoms such as irregular heartbeats, dizziness, or fainting, or in individuals that physicians suspect may have atrial fibrillation, many days of ECGs may be required for diagnosis—"long-term ECG recordings." These recordings must then undergo a time-consuming and human resource-intensive review to identify heart rhythm abnormalities.

8. Fertility tracking has increased in some states post-Dobbs

   The use of fertility-tracking technology increased in some states after the U.S. Supreme Court overturned Roe v. Wade despite warnings that reproduction-related data might not be secure, a new study has found.

9. AI enhances brain imaging—optimizing tractography for surgical procedures

   How can nerve pathways in the brain be visualized to improve the planning of complex surgeries? A research team from the Lamarr Institute and the University of Bonn, in collaboration with the Translational Neuroimaging Group at the Departments of Neuroradiology and Epileptology at the University Hospital Bonn (UKB), has investigated an AI-powered method that makes these reconstructions more precise. The study, recently published in NeuroImage: Clinical, could ultimately help make neurosurgical procedures safer.

10. Neuroscientists crack the code of how we make decisions with new mathematical framework

    A new mathematical model sheds light on how the brain processes different cues, such as sights and sounds, during decision making. The findings from Princeton neuroscientists may one day improve how brain circuits go awry in neurological disorders, such as Alzheimer's, and could help artificial brains, like Alexa or self-driving car technology, more helpful.

11. Modernizing the management of health records

    The U.S. health-care system exchanges tens of millions of patient records a day. Thanks to recent technological advances, the ability to analyze such large amounts of data has improved markedly.

12. Deadly diarrhea-causing contaminants may go undetected in flawed testing methods

    New research has uncovered critical gaps in an emerging global public health tool for detecting environmental fecal contamination—which is linked to life-threatening diarrheal diseases that cause 1.2 million deaths each year.

13. Neural waves study provides evidence that brain's rhythmic patterns play key role in information processing

    Researchers at the Ernst Strüngmann Institute in Frankfurt am Main, Germany, led by Wolf Singer, have made a new discovery in understanding fundamental brain processes. For the first time, the team has provided compelling evidence that the brain's characteristic rhythmic patterns play a crucial role in information processing. While these oscillatory dynamics have long been observed in the brain, their purpose has remained mostly elusive until now.

14. AI accelerates search for new tuberculosis drug targets

    Tuberculosis is a serious global health threat that infected more than 10 million people in 2022. Spread through the air and into the lungs, the pathogen that causes "TB" can lead to chronic cough, chest pains, fatigue, fever and weight loss. While infections are more extensive in other parts of the world, a serious tuberculosis outbreak currently unfolding in Kansas has led to two deaths and has become one of the largest on record in the United States.

15. AI tool converts data into 2D circular images, offering new way to visualize disease

    Mayo Clinic researchers have pioneered an artificial intelligence (AI) tool, called OmicsFootPrint, that helps convert vast amounts of complex biological data into two-dimensional circular images. The details of the tool are published in a study in Nucleic Acids Research.

16. AI and neural networks can reduce wait times for lesion classification in breast cancer patients

    One of the most agonizing experiences a cancer patient suffers is waiting without knowing: waiting for a diagnosis, waiting to get test results back, waiting to learn the outcome of treatment protocols. A new paper published in Scientific Reports evaluates the use of artificial intelligence (AI) and neural networks to significantly cut the time required for medical professionals to classify lesions in breast cancer ultrasound images.

17. Testing AI with AI: Ensuring effective AI implementation in clinical practice

    Using a pioneering artificial intelligence platform, Flinders University researchers have assessed whether a cardiac AI tool recently trialed in South Australian hospitals actually has the potential to assist doctors and nurses to rapidly diagnose heart issues in emergency departments.

18. Did COVID-19 prediction formulas miss something? New study suggests yes

    A new study published by an interdisciplinary team of researchers, including several from the University of Florida, suggests prediction models used by decision makers in attempting to control the spread of COVID-19 were incomplete.

19. AI tool helps find life-saving medicine for rare disease

    After combing through 4,000 existing medications, an artificial intelligence tool helped uncover one that saved the life of a patient with idiopathic multicentric Castleman's disease (iMCD). This rare disease has an especially poor survival rate and few treatment options. The patient could be the first of many to have their lives saved by an AI prediction system, which could potentially apply to other rare conditions.

20. Modeling study shows school closures' varied impact on COVID-19 outcomes across 74 countries

    School closures reduced the impact of COVID-19 in most countries but had negative effects in some, Monash University-led research encompassing 74 countries has found.

21. Physician's medical decisions can benefit from chatbot, study suggests

    Artificial intelligence-powered chatbots are getting pretty good at diagnosing some diseases, but how do chatbots do when the questions are less black-and-white? For example, how long before surgery should a patient stop taking prescribed blood thinners? Should a patient's treatment protocol change if they've had adverse reactions to similar drugs in the past? These sorts of questions don't have a textbook right or wrong answer—it's up to physicians to use their judgment.

22. How is AI influencing the field of medicine?

    The words "artificial intelligence" are now ubiquitous, their influence having risen dramatically in recent years and their impact being felt in health care and many other job sectors. In health care, artificial intelligence, or AI, has the potential to make drug discovery faster and cheaper and improve diagnoses and treatments. How should AI ensure patient privacy and how can it be used fairly? How might AI improve patient care, increase efficiency, and reduce costs? These are questions that need thoughtful discussion.

23. Preeclampsia prediction models lose accuracy after initial 48 hours, study finds

    The existing prediction models for severe complications of preeclampsia are most accurate only in the two days after hospital admission, with deteriorating performance over time, according to a study published February 4 in the open-access journal PLOS Medicine by Henk Groen of University of Groningen, the Netherlands, and colleagues.

24. Computational tool extracting disease-specific drug response signatures could improve cancer treatment discoveries

    A new computational tool could help researchers identify promising drug combinations for treating cancer, according to a new study.

25. Machine learning helps identify emergency department patients likely to have health-related social needs

    Addressing patients' health-related social needs such as housing instability, food insecurity, transportation barriers and financial strain is important to improving health outcomes, yet can be challenging. A recent study from Regenstrief Institute and Indiana University Indianapolis Richard M. Fairbanks School of Public Health investigates the best approach to predicting likely need for one or more health-related social need services.

26. AI-supported breast cancer screening—new results suggest even higher accuracy

    New research results now published from Lund University's MASAI trial are even better than the initial findings from last year: AI-supported breast screening detected 29% more cases of cancer compared with traditional screening. More invasive cancers were also clearly detected at an early stage using AI. Now the final part of the research study will focus on breast cancer missed by screening.

27. Automated voice analysis can diagnose multiple mental health disorders in one minute

    It's no secret that there is a mental health crisis in the United States. As of 2021, 8.3% of adults had major depressive disorder (MDD) and 19.1% had anxiety disorders (AD), and the COVID-19 pandemic exacerbated these statistics.

28. Conversational AI aids Alzheimer's caregivers with real-time assistance

    Imagine caring for a loved one with Alzheimer's disease, facing constant questions, challenges and uncertainty. Now, imagine having a trusted companion, a friendly and knowledgeable assistant, to guide you through each step.

29. All in the eyes: High-resolution retinal maps aid disease diagnoses

    Researchers have conducted one of the largest eye studies in the world to reveal new insights into retinal thickness, highlighting its potential in the early detection of diseases like type 2 diabetes, dementia and multiple sclerosis.

30. AI-powered map of the abdomen could help find cancer early on

    Radiologists are beginning to use AI-based computer vision models to help speed up the laborious process of parsing medical scans. However, these models require large amounts of carefully labeled training data to achieve consistent and accurate results, meaning radiologists must still dedicate significant time to annotating medical images.