Each year, medical diagnosis errors affect the health of millions of Americans and cost billions of dollars. Machine learning technologies can help identify hidden or complex patterns in diagnostic data to detect diseases earlier and improve treatments.
Several machine learning (ML) technologies are available in the U.S. to assist with the diagnostic process. The resulting benefits include earlier detection of diseases; more consistent analysis of medical data; and increased access to care, particularly for underserved populations. GAO identified a variety of ML-based technologies for five selected diseases
Most technologies relying on data from imaging such as x-rays or magnetic resonance imaging (MRI). However, these ML technologies have generally not been widely adopted.
Using artificial intelligence in health care seems like a futuristic concept, but it’s something that’s being used now to complement the knowledge of doctors. Radiology was one of the first areas that saw a lot of AI applications.
Dr. Bradley Erickson, director of Mayo Clinic’s AI Lab, says in the case of radiology, machine learning is used to complete some of the more time-consuming work. Beyond that, the diagnostic capabilities of AI are what attracts a lot of the appeal. While imaging-related AI has seen a lot of advancements, Dr. Bhavik Patel, director of AI at Mayo Clinic Arizona, says the next step is looking at AI applications for preventive health and shifting the mindset from pipeline to platform thinking.
There are a broad area of applications (for AI), starting in radiology, but really spreading into the rest of the clinic, including cardiology and even pathology.
“Colorectal cancer is almost entirely preventable with proper screening,” says senior author Michael B. Wallace, M.D., division chair of Gastroenterology and Hepatology at Sheikh Shakhbout Medical City in Abu Dhabi, United Arab Emirates, and the Fred C. Andersen Professor at Mayo Clinic in Jacksonville, Florida. “The substantial decrease in miss rate using AI reassures health care providers on the decreased risk of perceptual errors.”
The most relevant cause of post-colonoscopy colorectal cancer (CRC) is the miss rate of colorectal neoplasia — the rate at which neoplastic lesions are not detected in a screening or surveillance colonoscopy. Some studies suggest that 52% to 57% of post-colonoscopy CRC cases are due to missed lesions at patients’ colonoscopies. It’s estimated that 25% of neoplastic lesions are missed following screening colonoscopy.
Mayo Clinic Gastroenterology and Hepatology, in collaboration with colleagues from around the world, found that using artificial intelligence (AI) in colorectal cancer screening produced a 50% reduction in the miss rate for colorectal neoplasia. Results of the study were published in the July 2022 edition of Gastroenterology.
Are Amazon Alexa and Google Home limited to our bedrooms, or can they be used in hospitals? Do you envision a future where physicians work hand-in-hand with voice AI to revolutionize healthcare delivery? In the near future, clinical smart assistants will be able to automate many manual hospital tasks—and this will be only the beginning of the changes to come.
Voice AI is the future of physician-machine interaction and this Focus book provides invaluable insight on its next frontier. It begins with a brief history and current implementations of voice-activated assistants and illustrates why clinical voice AI is at its inflection point. Next, it describes how the authors built the world’s first smart surgical assistant using an off-the-shelf smart home device, outlining the implementation process in the operating room. From quantitative metrics to surgeons’ feedback, the authors discuss the feasibility of this technology in the surgical setting. The book then provides an in-depth development guideline for engineers and clinicians desiring to develop their own smart surgical assistants. Lastly, the authors delve into their experiences in translating voice AI into the clinical setting and reflect on the challenges and merits of this pursuit.
The world’s first smart surgical assistant has not only reduced surgical time but eliminated major touch points in the operating room, resulting in positive, significant implications for patient outcomes and surgery costs. From clinicians eager for insight on the next digital health revolution to developers interested in building the next clinical voice AI, this book offers a guide for both audiences.
Artificial intelligence reduced by twofold the rate at which precancerous polyps were missed in colorectal cancer screening, reported a team of international researchers led by Mayo Clinic. The study is published in Gastroenterology.
AI can pick up on subtle clues from a person’s physiological state such as their heart rate, the time differences between each heartbeat or the electrical signals their heart produces in order to identify irregularities that point to medical conditions.
“Being able to detect atrial fibrillation just by wearing a wristwatch all the time, that kind of relatively simple technology could actually have a massive impact,” explained consultant cardiologist Tim Fairbairn, cardiovascular imaging lead at Liverpool Heart and Chest Hospital in the UK.
AI has the power to transform health care. From more efficient diagnoses to safer treatments, it could remedy some of the ills suffered by patients. Film supported by @Maersk
Timeline: 00:00 – Can AI help heal the world? 00:45 – How can AI spot blindness? 04:01 – Protecting patients’ privacy 05:10 – How to share medical data safely 06:11 – Medical AI is rapidly expanding 08:02 – What do the sceptics say? 08.36 – Using AI for new medical devices 11:08 – What does the future hold for medical AI?