Artificial intelligence is more common in everyday applications than ever before. To optimize profits, path finding algorithms like map smartphone apps, Google searchs, YouTube recommendations, and even product positioning inside grocery shops are used. Some of these applications, however, are more life-preserving than others. In reality, some artificial intelligence applications are saving lives. In this article, we will look at some of these AI-based technologies and their impact on healthcare and life protection.
X-rays screening for certain diseases
According to a recent study led by Stanford University researchers, a new artificial intelligence algorithm can accurately screen chest X-rays for more than a dozen different types of disease and does it in less time than it takes to read this phrase.
According to the study, the algorithm, called CheXNeXt, is the first to concurrently assess X-rays for a variety of potential diseases and provide results that agree with radiologists’ readings.
The algorithm was trained by scientists to recognize 14 different pathologies: The algorithm outperformed radiologists for one disease, underperformed radiologists for three diseases, and performed equally well for ten diseases.
For example, on the basis of lung X-rays, a team of researchers has developed an artificial intelligence approach for screening COVID-19 patients. The system distinguishes between COVID-19 and non-COVID abnormalities using principal component analysis (PCA) and clustering approaches.
One of the most prevalent cancers is skin cancer. For instance, it can be challenging to discern between early-stage melanoma and benign moles and other malignant neoplasms. In order to get more decision support, doctors are testing out AI technologies.
InceptionV3 and ResNet50 architecture-based convolutional neural networks (CNNs) were employed by researchers in 2019 to analyze datasets from close-up and dermoscopic medical imaging.
They compared artificial intelligence’s performance to 95 dermatologists after training the model. And, the AI performed as accurately as human specialists.
Breast Cancer Detection
The largest study of its kind has discovered that artificial intelligence (AI) is demonstrating promising outcomes in identifying breast cancer that radiologists could have missed otherwise.
AI has been successfully used by German researchers to identify interval breast cancers, which can be ignored and misdiagnosed as false negative results because they grow between standard screening rounds, which is about every 24 months in many countries.
According to the peer-reviewed study, one in five interval cancers may be too faint to be seen by the human eye and can be missed by radiologists, which are referred to as “minimum symptoms,” while roughly 16% of interval cancers are presumably apparent during a previous screening.
According to the study, AI was able to accurately localize 27.5 percent of false-negative instances and just over 12 percent of early cancer symptoms on mammograms.
Ophthalmology diseases detection
According to research, the healthcare systems are under more stress to deliver timely ophthalmic care. The public healthcare systems worldwide can’t always ensure timely access to specialists due to rapidly aging populations.
Delays in ophthalmology care can result in permanent injury, such as loss of visual acuity and degeneration of the visual field, which could have been avoided with earlier diagnosis.
Other eye conditions include age-related macular degeneration (AMD), cataracts, glaucoma, and diabetic retinopathy (DR) also need for prompt treatment.
Use cases for new AI and deep learning models aim to narrow diagnostic gaps and speed up providers’ capacity to conduct population-level screenings.
The use of AI techniques can enhance glaucoma diagnostic procedures, according to a 2020 study. While using data from a single VF test, an algorithm trained on OCT images demonstrated to be more successful in predicting glaucoma progression early.
Also, a unique approach for remote cataract progression monitoring and intervention was put out by a group of Chinese researchers. They developed an AI helper for smartphones that patients may use for check-ins using deep learning algorithms.
Automation of Administrative Tasks in Healthcare
Healthcare providers may be able to significantly reduce operating expenses by utilizing AI technologies, notably OCR (Optical character recognition), for administrative healthcare duties.
Automation can be achieved by using machine learning and artificial intelligence for Prior authorizations, Healthcare claims management and even Patient records management.
Natural language processing algorithms can assist in gaining access to such data and converting it into insights for medical diagnostics and more general healthcare activities. Programs for patient care, improved patient resources, and tailored interventions are a few of these.
How does the future of AI in healthcare look like ?
New use cases for computer vision and artificial intelligence are being developed as a result of the expanding availability of annotated healthcare datasets.
The majority of new technologies have not yet been tried out in larger clinical settings. Healthcare AI appears to have a promising future.
A bigger revolution of the healthcare industry is also necessary for AI in healthcare to become widely used. Investments in modern technological systems and personnel training will be necessary for hospitals. They will have to work on extensive integration projects and concentrate on incorporating those technologies into their regular clinical practice.