A better tsunami early warning system could become a reality with the help of AI. Researchers have shown how widely-available AI technology can detect the subtle disturbances in the atmosphere caused when a tsunami's destructive waves begin to form far out to sea – a demonstration that could help provide earlier warnings for coastal communities long before the tsunami reaches them. "There is no global network for detecting tsunami waves, and installing physical hardware, like buoy-based systems, …

Summary: Artificial intelligence can detect signs of mild cognitive decline and Alzheimer's disease, even when no symptoms are apparent, by analyzing a person's speech. The technology could be used as a simple screening method to identify early signs of cognitive impairment. New technologies that can capture subtle changes in a patient's voice may help physicians diagnose cognitive impairment and Alzheimer's disease before symptoms begin to show, according to a UT Southwestern Medical Center researcher who led a study published in the Alzheimer's Association publication Diagnosis, Assessment & Disease Monitoring. "Our focus was on identifying subtle language and audio changes that are present in the very early stages of Alzheimer's disease but not easily recognizable by family members or an individual's primary care physician," said Ihab Hajjar, M.D., Professor of Neurology at UT Southwestern's Peter O'Donnell Jr. Brain Institute. Researchers used advanced machine learning and natural language processing (NLP) tools to assess speech patterns in 206 people – 114 who met the criteria for mild cognitive decline and 92 who were unimpaired.

DALLAS – April 12, 2023 – New technologies that can capture subtle changes in a patient's voice may help physicians diagnose cognitive impairment and Alzheimer's disease before symptoms begin to show, according to a UT Southwestern Medical Center researcher who led a study published in the Alzheimer's Association publication Diagnosis, Assessment & Disease Monitoring. "Our focus was on identifying subtle language and audio changes that are present in the very early stages of Alzheimer's disease but not easily recognizable by family members or an individual's primary care physician," said Ihab Hajjar, M.D., Professor of Neurology at UT Southwestern's Peter O'Donnell Jr. Brain Institute. Researchers used advanced machine learning and natural language processing (NLP) tools to assess speech patterns in 206 people – 114 who met the criteria for mild cognitive decline and 92 who were unimpaired. The team then mapped those findings to commonly used biomarkers to determine their efficacy in measuring impairment. Study participants, who were enrolled in a research program at Emory University in Atlanta, were given several standard cognitive assessments before being asked to record a spontaneous 1- to 2-minute description of artwork.

New technologies that can capture subtle changes in a patient's voice may help physicians diagnose cognitive impairment and Alzheimer's disease before symptoms begin to show, according to a UT Southwestern Medical Center researcher who led a study published in the journal Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring. "Our focus was on identifying subtle language and audio changes that are present in the very early stages of Alzheimer's disease but not easily recognizable by family members or an individual's primary care physician," said Ihab Hajjar, M.D., Professor of Neurology at UT Southwestern's Peter O'Donnell Jr. Brain Institute. Researchers used advanced machine learning and natural language processing (NLP) tools to assess speech patterns in 206 people--114 who met the criteria for mild cognitive decline and 92 who were unimpaired. The team then mapped those findings to commonly used biomarkers to determine their efficacy in measuring impairment. Study participants, who were enrolled in a research program at Emory University in Atlanta, were given several standard cognitive assessments before being asked to record a spontaneous 1- to 2-minute description of artwork.

People in the early stages of schizophrenia have telltale verbal signs that could be spotted by speech analysis software, long before doctors notice. Those who are developing the condition tend to be vaguer in their speech and to use words related to voices and sounds, a study has found. People with schizophrenia have delusions and hallucinations, such as hearing voices, as well as confusion and memory problems.

"Machine learning is being used in every aspect of health care. This particular model is focused on deep learning, which has had great success in many industries. However, in health care, we are on the front of pioneering deep learning and Edward is one of the first ones to apply it," Sun says.

NHS eye hospital Moorfields has announced it is working with DeepMind -- an artificial intelligence research lab acquired by Google in 2014 for a reported 400 million -- in a bid to identify people who are likely to lose their sight as a result of an eye disease. Through the medical research partnership, Moorfields will investigate whether DeepMind's AI technology can be used to help spot early signs of eye conditions that human eye care experts might miss. In order to determine whether DeepMind's AI technology is useful for diagnosing eye conditions, Moorfields is applying the company's algorithms to one million anonymous OCT (Optical Coherence Tomography) scans. The aim is to determine whether the algorithms can learn to spot early signs of age-related macular degeneration and sight loss that occurs as a result of diabetes. Mustafa Suleyman, Google DeepMind cofounder and head of DeepMind Health, told Business Insider that he wants DeepMind's AI to understand the structure and nature of eye scans "well enough to be able to try to predict in advance which ones indicate that a patient may be at risk to a particular kind of eye disease." The algorithms that DeepMind builds are known as machine learning algorithms because they have the ability to learn through training without being explicitly programmed.

Computers could soon be helping to diagnose cancer in patients with the help of artificial intelligence that has been trained to spots the early signs of the disease. An AI machine capable of accurately diagnosing breast cancer 92 per cent of the time has been developed by researchers. While it is still not quite as good as human specialists – who are correct 96 per cent of the time – it suggests that AI could soon be used to speed up and improve cancer screening. Scientists have used machine learning to create an artificial intelligence system capable of diagnosing breast cancer from lymph node biopsies with 92 per cent accuracy (cancer cells in a lymph node pictured). When combined with a human pathologist this accuracy increased to 99.5 per cent The system was developed by computer scientists at Harvard Medical School gave a machine learning algorithm slides of lymph nodes from breast cancer patients.