Rising to meet the formidable challenge of the timely diagnosis of dementia, research scientists from Regenstrief Institute, IUPUI and the medical schools of Indiana University and University of Miami are conducting the Digital Detection of Dementia study, a real-world evaluation of the use of an artificial intelligence (AI) tool they developed for early identification of Alzheimer's disease and related dementias in primary care, the setting where most adults receive healthcare.

Researchers have used virtual reality games, eye tracking and machine learning to show that differences in eye movements can be used to detect ADHD, potentially providing a tool for more precise diagnosis of attention deficits. Their approach could also be used as the basis for an ADHD therapy, and with some modifications, to assess other conditions, such as autism.

When a patient undergoes a surgical operation to remove a tumor or treat a disease, the course of surgery is often not predetermined. To decide how much tissue needs to be removed, surgeons must know more about the condition they are treating, including a tumor's margins, its stage and whether a lesion is malignant or benign - determinations that often hinge upon collecting, analyzing, and diagnosing a disease while the patient is on the operating table.

The artificial intelligence algorithms behind the chatbot program ChatGPT - which has drawn attention for its ability to generate humanlike written responses to some of the most creative queries - might one day be able to help doctors detect Alzheimer's Disease in its early stages. Research from Drexel University’s School of Biomedical Engineering, Science and Health Systems recently demonstrated that OpenAI's GPT-3 program can identify clues from spontaneous speech that are 80% accurate in predicting the early stages of dementia.

Using machine learning and clinical data from electronic health records, researchers at the Icahn School of Medicine at Mount Sinai in New York constructed an in silico, or computer-derived, marker for coronary artery disease (CAD) to better measure clinically important characterizations of the disease.

At any time, most cancer patients are receiving a treatment that does not significantly benefit them while enduring bodily and financial toxicity. Aiming to guide each patient to the most optimal treatment, precision medicine has been expanding from genetic mutations to other drivers of clinical outcome. There has been a concerted effort to create "avatars" of patient tumors for testing and selecting therapies before administering them into patients.

Researchers from the University of Jyväskylä and the Central Finland Health Care District have developed an AI based neural network to detect an early knee osteoarthritis from x-ray images. AI was able to match a doctors’ diagnosis in 87% of cases. The result is important because x-rays are the primary diagnostic method for early knee osteoarthritis.