Deep learning is a computer science field that involves training neural nets by feeding them vast amounts of data. In the past few years, this AI-backed technology has become one of the most exciting areas in tech. As we work toward higher adoption of artificial intelligence, many companies are applying AI-powered technologies to solve real-world problems. Public healthcare, drug discovery, clinical trials, and medical imaging are just a sliver of all AI applications in the medical sector.
The market of expert systems in healthcare
Over the last few years, AI and its offshoots have reached unbelievable heights in healthcare. Thanks to unmatched data processing capabilities, smart algorithms can pull together patient insights and generate actionable and data-driven predictions.
According to Statista, 41% of US healthcare leaders reported their AI use was at a fully functional level as of 2021. At the same time, around 60% of companies report that the smart adoption in medical-related fields such as pharma helps improve quality control, while 42% of respondents think that monitoring and diagnosis are among other important applications for intelligent technologies in healthcare.
The many faces of machine intelligence
Deep learning is a form of AI that involves training neural networks on large datasets and using them to crunch data and produce forecasts. In other words, it’s a smart system that can learn by itself without being explicitly programmed with rules or procedures.
Compared with machine learning, deep learning is suitable for crunching unstructured data. Deep learning models mimic the human brain’s capacity, thus being more effective in solving multi-format data challenges.
Smart technologies for medical imaging
Machine learning medical imaging is one of the most influential areas for deep learning. It is used in many fields, including oncology, radiology, nuclear medicine, and cardiology. Deep learning is a type of machine learning algorithm that has been used to improve medical imaging technology.
Deep learning algorithms are trained using large amounts of data that has been collected from scanning equipment or collected by doctors in the form of images or X-rays. This type of training allows the algorithms to learn more complex patterns based on data that has already been collected.
Deep learning can be used for different medical imaging applications such as:
- Predicting cancer patients’ response to therapy;
- Understanding patterns in brain activity;
- Identifying malignant tumors at early stages;
- MRI image processing acceleration;
- Retinal blood vessel segmentation, and others.
Why is deep learning beneficial for medical imaging?
AI-powered technologies have the ability to improve healthcare due to unrivaled real-time processing capabilities and predictive power. Let’s see what other unique benefits AI-based medical imaging brings to the healthcare sector.
Studies show that AI-supported technology boosts workflow productivity by cutting the entire report reading time by 34% and has 97% to 99% accuracy. According to the user’s clinical context, radiologists can alter the detectable findings and the ways in which they are visualized.
An MRI scan can take between 15 to 90 minutes depending on which part of your body needs to be scanned. Smart technologies can speed up MRI analysis by automatically processing data and extracting valuable insights at higher speeds with higher accuracy.
Earlier detection of cancers
According to a study, compared to conventional methods, AI-based models can more accurately predict occult nodal metastases in patients with a particular early-stage oral cavity cancer. Deep learning is also effective for identifying abnormal or anomalous tissues at early stages.
The final word
Deep learning is a type of machine intelligence that has emerged as one of the most important areas of AI research in recent years. Deep neural networks have revolutionized many fields including computer vision and medical imaging, among others. AI-based technologies also have the potential to revolutionize drug discovery by automating many of the traditional steps in this process.