How AI is Already Helping Diagnosis of Diseases Faster?
6 min read
AI's function in disease detection is more than just a future promise; it is already improving the capacities of healthcare workers now. In fact, AI is poised to fully overhaul the medical diagnosis industry. With each passing day, AI is being utilized to detect diseases faster, save lives, and improve the quality of treatment for people worldwide.
Many experts (including some at AI Authority, the emerging AI publication) think that the most recent AI achievements in disease diagnosis are only the tip of the iceberg, and that as AI technology advances, its impact on healthcare will rise tremendously.
5 ways AI is already helping diagnosis of disease faster
The following is a list of medical sector developments that demonstrate the use of artificial intelligence and machine learning technology to identify diseases more quickly and save lives.
Paige.AI has a potent idea in oncology
Paige's objective is both ambitious and transformative: to create and offer next-generation digital diagnostics and predictive testing using cutting-edge tissue-based artificial intelligence. Paige is expediting cancer diagnosis with an AI-enabled, web-based platform, giving pathologists throughout the world the tools they need to make faster, more accurate diagnoses, eventually improving patient outcomes.
Paige's invention revolves around the creation of large model AI systems specifically designed for cancer detection and quantification in tissue biopsies. Paige's AI algorithms were trained on an extensive dataset consisting of over 25 million glass slides, utilizing the world's largest digital image database of cancer cases and genomic data.
Paige can utilize this data set, as well as accompanying pathology reports from many global sources, to create AI models that are not only resilient but also extremely exact. Here's more on how Paige is using AI for advanced diagnosis.
Also Read: How AI is improving cancer treatment
Viz.AI for a faster and smarter healthcare system
The motivation for Viz.ai came from a patient who had a successful brain surgery but died tragically because the procedure was conducted too late. This terrible ending was caused by avoidable procedure delays, which jeopardized the efforts of the doctors involved.
Determined to find a better solution, neurosurgeon Dr. Chris Mansi teamed up with machine learning post-doctoral Dr. David Golan. Together, they founded Viz.ai with a single objective in mind: to employ artificial intelligence to make healthcare work faster and smarter, not just change it, but completely transform it.
Viz.ai's software quickly analyzes CT scans to detect early indicators of vascular occlusion strokes and alerts doctors via their phones. This rapid alert system significantly decreases the time required to get patients into surgery when compared to older approaches.
The Viz.ai One solution, which integrates smoothly into EHR systems and radiology worklists, offers intelligent care paths for neurological, vascular, and cardiac illnesses. It applies the appropriate AI algorithms to patient images or data in real time and ensures that radiologists and specialists are aware of any suspected indicators of disease within seconds.
Clinical trials have shown that hospitals that use Viz.ai's technology can cut the time between a patient's arrival and their first examination by a brain surgery specialist by roughly 40 minutes. The platform also results in a 40% reduction in impairment after 90 days, a 2.5-day reduction in hospital length of stay, and a 3.5-day reduction in ICU length of stay. Furthermore, more than 90% of Viz.ai notifications are examined within 5 minutes, assuring timely action and improved patient outcomes.
Image analysis powered by AI comes to the rescue
Researchers at the Beckman Institute for Advanced Science and Technology have created an AI model that has the potential to change disease and tumor diagnosis. This ground-breaking technology not only diagnoses problems in medical photos with high accuracy, but also produces a visual map to explain each diagnosis.
The model, developed by Sourya Sengupta and Mark Anastasio, aims to detect cancer and disease in their early stages. Sengupta's model solves this issue by providing an equivalency map (E-map) that identifies the parts of an image that contributed the most to diagnosis.
The researchers trained their AI model on over 20,000 photos for three separate diagnostic tasks: identifying cancers in mammograms, finding Drusen in retinal OCT images (an early marker of macular degeneration), and diagnosing cardiomegaly (heart enlargement) from chest X-rays.
The model performed admirably, with accuracy rates comparable to existing black-box AI systems: 77.8% for mammograms, 99.1% for retinal OCT pictures, and 83% for chest X-rays.
Apollo Hospital and Google join hands for the early detection of diseases
Apollo Hospitals and Google have collaborated to use AI-powered screening technology for the early identification of tuberculosis (TB), lung cancer, and breast cancer. Apollo Radiology International plans to provide three million free screenings to impoverished populations. This project will considerably improve early diagnosis and treatment, and it is only one of many examples in India where medical specialists are increasingly using AI to better diagnosis.
Read more on this here.
Deep learning AI model is helping with improved diagnosis
Researchers at Harvard Medical School and Brigham and Women's Hospital created SISH (self-supervised image search for histology), a deep-learning algorithm that serves as a search engine for pathology images. SISH can detect unusual diseases and predict treatment outcomes by searching vast picture libraries for similar cases that do not require manual annotation.
It was tested on over 22,000 patient cases and proved fast and accurate retrieval of relevant photographs. This application promises to improve pathology training, illness diagnosis, and patient care by effectively navigating large image libraries.
Another outstanding example is Enlitic, a business that pioneered the application of deep learning algorithms to speed up the analysis of medical image data.
Enlitic's technology has proved the ability to analyze chest X-rays, CT scans, and other radiological images at unprecedented speeds and precision, allowing healthcare providers to make more prompt and informed judgments regarding their patients' illnesses.
Also Read: How AI is helping invent more medicines
In the end!
The preceding examples demonstrate that AI has advanced to the point where it can no longer be ignored in terms of improving healthcare and diagnostics.
Tech giants like Google, Microsoft, and IBM have all forged partnerships with renowned medical institutions, combining their respective expertise in AI and healthcare to drive the development and deployment of next-generation diagnostic solutions.
These combined efforts have resulted in a plethora of case studies and pilot programs that show the practical application of AI-based diagnostics in real-world healthcare settings.
This demonstrates that progress is still underway, and AI will be increasingly adopted by businesses and specialists to provide better diagnostic solutions.
If you come across any exceptional AI-powered diagnostic ideas, please let us know in the comments section. AI Authority would love to look deeper into them and bring them to the readers' attention.