Interoperability on Artificial intelligence
September 1, 2023Demis Hassabis
September 1, 2023Unveiling the Future of Healthcare: The Latest AI Marvels in Medicine – Your Essential AI in Medicine Newsletter!
Welcome to the latest edition of the Newsletter, where we bring you the freshest updates and insights on cutting-edge advancements at the intersection of artificial intelligence and healthcare...
Welcome to the latest edition of the Newsletter, where we bring you the freshest updates and insights on cutting-edge advancements at the intersection of artificial intelligence and healthcare. In this issue, we deep inside the most recent breakthroughs, innovations, and trends shaping the medical AI landscape. From groundbreaking diagnostic tools to revolutionary treatment approaches, stay tuned as we uncover the latest news that reshapes how we approach healthcare and provide the path for a healthier future.
Sonio’s AI-powered Quality Control Software Revolutionizes Medical Imaging in Prenatal Care
Paris-based femtech startup Sonio has achieved a groundbreaking milestone with FDA approval for their innovative quality control software, Sonio Detect. This AI-driven solution revolutionizes fetal ultrasounds by offering manufacturer-agnostic enhancements for accuracy and efficiency. The software expedites the identification of critical structures during ultrasounds, aiding healthcare professionals and bolstering diagnostic confidence. Validation on a 17,000-image dataset underscores its reliability, while Sonio’s CE-marked product, Sonio Diagnostics, aids practitioners in identifying symptoms and abnormalities during ultrasounds. The company’s recent $14 million Series A funding highlights a growing interest in AI-driven medical imaging. Sonio’s advancements align with the AI trend, transforming medical imaging and patient care. CEO Cecile Brosset envisions their technology becoming a staple for maternal care, reshaping medical imaging’s future.(1)
Paris-based femtech startup Sonio has achieved a groundbreaking milestone with FDA approval for their innovative quality control software, Sonio Detect. This AI-driven solution revolutionizes fetal ultrasounds by offering manufacturer-agnostic enhancements for accuracy and efficiency. The software expedites the identification of critical structures during ultrasounds, aiding healthcare professionals and bolstering diagnostic confidence. Validation on a 17,000-image dataset underscores its reliability, while Sonio’s CE-marked product, Sonio Diagnostics, aids practitioners in identifying symptoms and abnormalities during ultrasounds.
The company’s recent $14 million Series A funding highlights a growing interest in AI-driven medical imaging. Sonio’s advancements align with the AI trend, transforming medical imaging and patient care. CEO Cecile Brosset envisions their technology becoming a staple for maternal care, reshaping medical imaging’s future.(1)
How old are you? AI can estimate your actual age by looking at your chest.
Scientists at Osaka Metropolitan University have developed an advanced AI model that uses chest radiographs to accurately estimate a person’s chronological age and identify potential correlations with chronic diseases. The breakthrough research led by Yasuhito Mitsuyama and Dr. Daiju Ueda from the Department of Diagnostic and Interventional Radiology involved constructing a deep learning-based AI model trained on data from multiple institutions. The model demonstrated a strong correlation coefficient of 0.95 between AI-estimated age and actual age. Validation using radiographs from patients with known diseases revealed that higher differences between AI-estimated age and actual age were linked to various chronic conditions like hypertension and chronic obstructive pulmonary disease. This innovation can revolutionize early disease detection and intervention, offering insights into health conditions beyond chronological age. The findings are due to be published in The Lancet Healthy Longevity.(2)
Scientists at Osaka Metropolitan University have developed an advanced AI model that uses chest radiographs to accurately estimate a person’s chronological age and identify potential correlations with chronic diseases. The breakthrough research led by Yasuhito Mitsuyama and Dr. Daiju Ueda from the Department of Diagnostic and Interventional Radiology involved constructing a deep learning-based AI model trained on data from multiple institutions. The model demonstrated a strong correlation coefficient of 0.95 between AI-estimated age and actual age.
Validation using radiographs from patients with known diseases revealed that higher differences between AI-estimated age and actual age were linked to various chronic conditions like hypertension and chronic obstructive pulmonary disease. This innovation can revolutionize early disease detection and intervention, offering insights into health conditions beyond chronological age. The findings are due to be published in The Lancet Healthy Longevity.(2)
AI tools can identify social determinants of health in dementia patients.
In a rush to utilize AI and machine learning for enhanced hospital efficiency, a study highlights their potential in identifying non-medical needs that impact patient health and care accessibility. Focusing on Alzheimer’s and dementia patients, the research reveals a rule-based natural language processing tool’s success in identifying issues like transportation access, food insecurity, social isolation, and abuse signs. The study, led by Elham Mahmoudi and Wenbo Wu, underscores the tool’s value for proactive care addressing social determinants, aiding clinicians and social workers despite some limitations in identifying housing and medication needs. The researchers are working to validate the algorithm against a social determinants questionnaire and plan a pilot program to connect identified patients with community resources.(3)
In a rush to utilize AI and machine learning for enhanced hospital efficiency, a study highlights their potential in identifying non-medical needs that impact patient health and care accessibility. Focusing on Alzheimer’s and dementia patients, the research reveals a rule-based natural language processing tool’s success in identifying issues like transportation access, food insecurity, social isolation, and abuse signs.
The study, led by Elham Mahmoudi and Wenbo Wu, underscores the tool’s value for proactive care addressing social determinants, aiding clinicians and social workers despite some limitations in identifying housing and medication needs. The researchers are working to validate the algorithm against a social determinants questionnaire and plan a pilot program to connect identified patients with community resources.(3)
Hyfe AI and ActiGraph team up to develop a cough detection tool.
Hyfe AI and ActiGraph are collaborating to enhance cough data collection in clinical trials by combining Hyfe’s cough detection software with ActiGraph’s wearable technology. Cough, a common symptom indicative of various underlying conditions, is typically measured using bulky recording devices in clinical trials. This partnership aims to transform this approach by integrating Hyfe’s AI-powered acoustic technology into ActiGraph’s wireless device, which can already measure physiological parameters like heart rate, oxygen saturation, and blood pressure. The device, FDA-cleared for activity and sleep monitoring, will now detect cough frequency and patterns, offering a remote monitoring solution for research in conditions such as chronic cough, asthma, and heart failure. This collaboration could provide more comprehensive and meaningful data for clinical trial sponsors and the pharmaceutical industry. (4)
Hyfe AI and ActiGraph are collaborating to enhance cough data collection in clinical trials by combining Hyfe’s cough detection software with ActiGraph’s wearable technology. Cough, a common symptom indicative of various underlying conditions, is typically measured using bulky recording devices in clinical trials. This partnership aims to transform this approach by integrating Hyfe’s AI-powered acoustic technology into ActiGraph’s wireless device, which can already measure physiological parameters like heart rate, oxygen saturation, and blood pressure.
The device, FDA-cleared for activity and sleep monitoring, will now detect cough frequency and patterns, offering a remote monitoring solution for research in conditions such as chronic cough, asthma, and heart failure. This collaboration could provide more comprehensive and meaningful data for clinical trial sponsors and the pharmaceutical industry.(4)
How AI Could Help Predict—and Avoid—Sports Injuries, Boost Performance
Artificial Intelligence (AI) is transforming sports medicine, particularly in injury risk assessment, where it offers unparalleled advantages in predicting, preventing, and managing sports-related injuries. In contrast to traditional methods reliant on subjective judgment, AI’s data-crunching capabilities allow for precise analysis of biomechanics, physiological data, and training load, enabling the identification of risk factors and early intervention. Wearable tech with AI monitors an athlete’s real-time physiological parameters to detect overtraining signs while AI scrutinizes movement patterns for biomechanical anomalies.Additionally, AI aids in injury management through precise diagnoses, personalized treatment, and recovery monitoring using imaging test data. Despite challenges like privacy concerns and prediction reliability, AI’s integration promises improved training, prevention, rehabilitation strategies, and advances in sports science. The AI revolution in sports medicine changes the game and athletes’ lives worldwide.(5)
Artificial Intelligence (AI) is transforming sports medicine, particularly in injury risk assessment, where it offers unparalleled advantages in predicting, preventing, and managing sports-related injuries. In contrast to traditional methods reliant on subjective judgment, AI’s data-crunching capabilities allow for precise analysis of biomechanics, physiological data, and training load, enabling the identification of risk factors and early intervention. Wearable tech with AI monitors an athlete’s real-time physiological parameters to detect overtraining signs while AI scrutinizes movement patterns for biomechanical anomalies.
Additionally, AI aids in injury management through precise diagnoses, personalized treatment, and recovery monitoring using imaging test data. Despite challenges like privacy concerns and prediction reliability, AI’s integration promises improved training, prevention, rehabilitation strategies, and advances in sports science. The AI revolution in sports medicine changes the game and athletes’ lives worldwide.(5)
Mount Sinai Launches Center for Ophthalmic Artificial Intelligence and Human Health
The Icahn School of Medicine at Mount Sinai has launched the pioneering Center for Ophthalmic Artificial Intelligence and Human Health, the first in New York and among the first in the United States. Collaborating with the Windreich Department of Artificial Intelligence and Human Health, the Center focuses on advancing AI in ophthalmology, solidifying Mount Sinai’s reputation for innovative patient care. By merging AI into education, research, and clinical practice, the Center accelerates diagnosing eye conditions, including diabetic retinopathy and macular degeneration. It detects cardiovascular and neurological issues linked to eye health.
Led by Dr. James C. Tsai with Co-Directors Dr. Louis Pasquale and Dr. Alon Harris, the Center will implement AI models in areas such as tele-retina, ophthalmology teleconsultation, and eye stroke service, transforming diagnostics, triage, and treatment with unprecedented precision and efficiency. This endeavor cements Mount Sinai as a trailblazer in AI-driven ophthalmology, offering novel approaches to complex health issues and training future leaders in the field.
The Icahn School of Medicine at Mount Sinai has launched the pioneering Center for Ophthalmic Artificial Intelligence and Human Health, the first in New York and among the first in the United States. Collaborating with the Windreich Department of Artificial Intelligence and Human Health, the Center focuses on advancing AI in ophthalmology, solidifying Mount Sinai’s reputation for innovative patient care.
By merging AI into education, research, and clinical practice, the Center accelerates diagnosing eye conditions, including diabetic retinopathy and macular degeneration. It detects cardiovascular and neurological issues linked to eye health. Led by Dr. James C. Tsai with Co-Directors Dr. Louis Pasquale and Dr. Alon Harris, the Center will implement AI models in areas such as tele-retina, ophthalmology teleconsultation, and eye stroke service, transforming diagnostics, triage, and treatment with unprecedented precision and efficiency. This endeavor cements Mount Sinai as a trailblazer in AI-driven ophthalmology, offering novel approaches to complex health issues and training future leaders in the field.