If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
In order to better prevent and cure debilitating ailments including Alzheimer's disease, schizophrenia, and autism, new research at Georgia State University's TReNDS Center may result in early detection. For a recent study published in Nature Scientific Reports, a team of seven scientists from Georgia State University created a sophisticated computer program to sift through enormous amounts of brain imaging data and discover unexpected patterns related to mental health disorders. The brain imaging data were generated using functional magnetic resonance imaging (fMRI) scans, which measure dynamic brain activity by detecting minute fluctuations in the flow of blood. Although exceedingly complicated, brain dynamics are the key to understanding how the brain functions and malfunctions. Functional magnetic resonance imaging resting-state dynamics are noisy, high-dimensional, and difficult to understand.
We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. The human brain has long been, and continues to be, a conundrum -- how it developed, how it continues to evolve, its tapped and untapped capabilities. The same goes for artificial intelligence (AI) and machine learning (ML) models. And just as the human brain created AI and ML models that grow increasingly sophisticated by the day, these systems are now being applied to study the human brain itself. Specifically, such studies are seeking to enhance the capabilities of AI systems and more closely model them after brain functions so that they can operate in increasingly autonomous ways.
One of the primary challenges faced by researchers and clinicians seeking to study mental health is that direct observation of indicators of mental health issues can be challenging, as a diagnosis often relies on either self-reporting of specific feelings or actions, or direct observation of a subject (which can be difficult due to time and cost considerations). That is why there has been a specific focus over the past two decades on deploying technology to help human clinicians identify and assess mental health issues. Between 2000 and 2019, 54 academic papers focused on the development of machine learning systems to help diagnose and address mental health issues were published, according to a 2020 article published in ACM Transactions on Computer-Human Interaction. Of the 54 papers, 40 focused on the development of a machine learning (ML) model based on specific data as their main research contribution, while seven were proposals of specific concepts, data methods, models, or systems, and three applied existing ML algorithms to better understand and assess mental health, or improve the communication of mental health providers. A few of the papers described the conduct of empirical studies of an end-to-end ML system or assessed the quality of ML predictions, while one paper specifically discusses design implications for user-centric, deployable ML systems.
Artificial intelligence (AI) has the potential to play a role in predictive medicine, from prevention and diagnosis to treatment. Machine learning models have proved useful in certain types of leukemia and deep learning in diabetic retinopathy. However, contrary to expectations of human bias removal, evidence has shown an increased bias, and hence unfairness, against specific subpopulations. The problem arises because AI programs learn from data and they will simply learn differently depending on the datasets physicians or researchers employ to train them. A study published in Science (open access) this week investigates the bias in AI models used to predict cognitive, behavioral, and psychiatric patterns that may characterize a disorder. Jingwei Li and collaborators examined whether white Americans and African Americans enjoyed similar predictive performance when the AI models were trained with state-of-the-art large-scale datasets containing neuroimaging and behavioral data.
A desire to achieve large medical imaging datasets keeps increasing as machine learning algorithms, parallel computing, and hardware technology evolve. Accordingly, there is a growing demand in pooling data from multiple clinical and academic institutes to enable large-scale clinical or translational research studies. Magnetic resonance imaging (MRI) is a frequently used, non-invasive imaging modality. However, constructing a big MRI data repository has multiple challenges related to privacy, data size, DICOM format, logistics, and non-standardized images. Not only building the data repository is difficult, but using data pooled from the repository is also challenging, due to heterogeneity in image acquisition, reconstruction, and processing pipelines across MRI vendors and imaging sites. This position paper describes challenges in constructing a large MRI data repository and using data downloaded from such data repositories in various aspects. To help address the challenges, the paper proposes introducing a quality assessment pipeline, with considerations and general design principles.
Breast cancer is the second most common cancer globally, and is the most commonly diagnosed cancer in Indian women. Of the 685,000 women who die around the world every year because of breast cancer, over 90,000 are in India, where cancer of the breast is the most common cause of cancer-related deaths in India. One of the major reasons for the high mortality rate in India is that most Indian patients present in the later stages of the disease. Population-scale screening with early detection methods, and efforts to increase awareness of breast cancer, could help tackle the disease, improve survival rates and reduce treatment costs. Screening mammography is a widely used method, but its usage in low- and middle-income countries (LMICs) is limited due to equipment cost and the expert skill required for interpretation of mammograms.
The advent of new field strengths of up to 10.5 Tesla allows magnetic resonance imaging in unprecedented detail. This opens up enormous opportunities in cardiac, neurological, and experimental medicine. Researchers will discuss these new possibilities at the MDC's Ultrahigh Field Magnetic Resonance Symposium on September 2 and 3. The use of magnetic resonance imaging (MRI) at 1.5 Tesla has long been a standard part of clinical practice. And about one in five major hospitals already has a 3-Tesla machine.
Scientists believe they may have discovered the'cornerstone of human intelligence', and it is all down to how we create and store memories. Previous research shows animals use a technique called'pattern separation' which stores memories in separate groups of neurons in the hippocampus. This stops them from getting mixed up, and it was believed humans probably use this technique as well. But a new study by experts at the University of Leicester shows the same group of neurons in the hippocampus store all memories. This key difference, the researchers say, could be the single factor which allowed our intellect to surpass that of other animals.
MRI scans take time but offer one of the most detailed pictures of the inside of a patient's body - now thanks to artificial intelligence the process will be much faster. Facebook have been working with experts from New York University to create a'free and open source' AI model that can be used on almost all existing MRI scanners. It works by taking a less detailed scan of the body - that doesn't take as long to complete - and then acts to'fill in the gaps of missing information'. The model has been trained using thousands of full MRI scans and during a blind test of six radiographers they all found the fast MRI scan accurate and higher quality. Researchers say this technology is particularly useful for getting detailed scans of children who would otherwise struggle to stay still for long enough in the machine.
PHILADELPHIA - To answer medical questions that can be applied to a wide patient population, machine learning models rely on large, diverse datasets from a variety of institutions. However, health systems and hospitals are often resistant to sharing patient data, due to legal, privacy, and cultural challenges. An emerging technique called federated learning is a solution to this dilemma, according to a study published Tuesday in the journal Scientific Reports, led by senior author Spyridon Bakas, PhD, an instructor of Radiology and Pathology & Laboratory Medicine in the Perelman School of Medicine at the University of Pennsylvania. Federated learning -- an approach first implemented by Google for keyboards' autocorrect functionality -- trains an algorithm across multiple decentralized devices or servers holding local data samples, without exchanging them. While the approach could potentially be used to answer many different medical questions, Penn Medicine researchers have shown that federated learning is successful specifically in the context of brain imaging, by being able to analyze magnetic resonance imaging (MRI) scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.