My top 5 deep learning applications in healthcare
I believe one of the best uses of deep learning is in healthcare, where you can significantly improve patient care and even actually save people’s lives.
The healthcare industry is one of the most data-intensive industries in the world. This huge amount of information can be used for many different applications, including image processing; diagnosis and treatment; drug discovery; assistive surgery; and biohacking.
1. Image processing
Image processing is a hot area of research in artificial intelligence, and there are many applications throughout industry. It’s used in healthcare to detect abnormalities in medical images, such as tumors or blood vessels that might be missed by other techniques. It’s also used for image classification — to identify objects within an image based on their appearance and location. For example, in radiology, it’s used to diagnose cancer at an earlier stage by identifying suspicious regions on X-ray images
In addition to these uses, image processing could allow us to develop better AI models for health care applications such as diagnosis or treatment planning (e.g., “what kind of medication should I prescribe?”).
2. Diagnosis and treatment
AI can help doctors make better decisions.
For example, patients with chronic illnesses such as diabetes or asthma may be able to predict when their condition will worsen based on historical trends rather than waiting until they’re admitted into the hospital for treatment. Similar methods are used by hospitals that want more effective ways of caring for patients who have pre-existing conditions (such as heart disease or cancer). The goal here is not just treating symptoms but preventing future complications from occurring — which requires an understanding of how each individual responds differently depending on his/her genetics, lifestyle factors like diet and exercise habits etc., so doctors can tailor treatments accordingly depending on these factors instead of just prescribing generic drugs which may not work well enough against certain types.
In addition, AI will provide access for patients who want this kind of information but don’t know where else it might be found; this could be especially useful for patients with rare diseases (for instance), who may not have heard about certain treatments before now because there aren’t many doctors practicing in their area with specialized knowledge around those cases (and so don’t see them coming up during regular checkups).
3. Drug discovery
Drug discovery is a complex process that involves identifying new drugs, predicting how they will interact with the human body and predicting the efficacy and side effects of those drugs. Deep learning can help speed up this process by giving researchers more accurate models for testing drug candidates.
Researchers are already using deep learning to analyze data from clinical trials and identify patterns that predict which patients will respond well or poorly to certain treatments. By training their algorithms on large datasets, scientists can also learn about how drugs work within different populations — such as whether people who take statins have an increased risk of developing erectile dysfunction (ED), or if people with diabetes should avoid aspirin because they might be more likely to bleed while taking it than those without diabetes.
4. Assistive surgery
Assistive surgery is the practice of using deep learning to assist surgeons in pre-operative planning. The technology can help identify the best course of action for a particular patient, whether it be determining whether a hip replacement is necessary or determining which type of surgical procedure is most appropriate for their condition.
This type of technology has long been used in industrial applications like manufacturing lines and warehouses, but it’s now also being used by hospitals across the country as they seek ways to streamline operations and reduce costs. Besides saving money, the technology can also help to ensure that patients receive the best care possible. By helping doctors and surgeons identify the best course of action for each patient, machine learning can help reduce unnecessary surgeries and prevent medical errors.
Another use case of assistive surgery is robotic surgery (or robot-assisted surgery). As the term suggests, it’s a type of surgical procedures that are done using robotic systems. It allows doctors to perform many types of delicate and complex procedures, typically requiring very small incisions and large magnification, with more precision, flexibility and control during the operation. Often, robotic surgery makes minimally invasive surgery possible, resulting in fewer risks of complications, less pain and blood lost, shorter hospital stay and faster recovery times, and smaller, less noticeable scars.
5. Biohacking
Biohacking is one of my favorite AI applications in healthcare but perhaps more controversial. Yet, Gartner says AI and biohacking will shape the future of tech.
Biohacking is the practice of augmenting one’s body with technology. The focus of this field is to improve athletic performance, cognitive function and more.
Biohacking uses machine learning techniques to analyze data from an individual’s body and utilize it to help them achieve their goals. This can include things like tracking heart rate variability (HRV) or sleep quality at night; analyzing muscle mass; identifying biomarkers that indicate illness or other potential issues; measuring blood pressure fluctuations throughout the day, week or month depending on how long you’ve been exercising regularly etc; and one of my favorites being brain-computer interface (BCI) for more personalized neuro-prosthetics, “mind-reading”, and other use cases such as what Neuralink is doing.
The goal of biohacking is to provide a better understanding of how the body works and how we can improve it. It’s about using technology to help us become more efficient and healthy in order to perform better in all aspects of life.
Deep learning is a key component of the healthcare industry, and it will soon become a necessary component of every hospital CIO’s toolkit.
As the healthcare industry continues to adopt deep learning, it will be important for hospitals and other healthcare providers to understand how deep learning can help advance patient care. As we’ve seen throughout this article, there are many different ways in which deep learning can be applied to healthcare, from image processing, diagnosis and treatment, drug discovery to assisting surgery and even biohacking. And there are definitely more opportunities for medical professionals with this technology!
