Traditional artificial intelligence (AI) and generative AI tools have transformed the way we live and work. This is especially true for doctors and healthcare workers, who have seen technology revolutionize their industry over the past few years. AI-powered technology helps with everything from patient care to physician burnout. Generative AI tools provide even more opportunities in the healthcare field, helping with everything from automating tasks to summarizing patient consultations.

Ahead, we break down the types of AI that are revolutionizing the healthcare industry. We also explore the benefits of AI in healthcare, the ethical issues of using AI in healthcare and how these tools will transform the medical industry in the future.

Types of AI That Can Be Used in Healthcare

The use of AI is usually associated with business and technology companies that have brought this technology to market. However, many types of AI are being used in the healthcare industry today. Some help improve patient outcomes, and others provide other benefits to healthcare like creating efficiencies in health systems.

Deep Learning Algorithms

Deep learning (DL) algorithms stem from machine learning, a subset of AI focused on training technology to mirror human decision-making. This approach uses deep neural networks to power many functions, like automation, insight development and even physical functions used in automated vehicles. These artificial neural networks are based on mathematical models structured similarly to the neural networks in our brains. They require large amounts of data as inputs, which is why this field has evolved with our ability to process and store a lot of data.

The capacity of neural networks to process large datasets has been applied in many areas of healthcare. Here are some of the most common applications of DL in healthcare today: 

• Medical imaging
• Electronic health records
• Genomics (study of all the genes in an organism)
• Drug development

DL is often combined with other forms of AI to help them process data. This creates a foundation for innovation in AI and healthcare.

Natural Language Processing

Natural language processing (NLP) is a form of AI used to interpret, process and manipulate human language. NLP models also apply meaning to words and pick up language patterns. This is especially useful in applications and websites that use chatbot features to interact with users.

NLP is used in the healthcare industry in a variety of ways. For example, clinicians use it to interpret notes in medical records, predict patient mortality in critical care and interact with patients using this type of AI. The ability to communicate with patients is crucial in the medical field, as is the ability of medical practitioners to communicate with each other. This makes NLP an essential application of AI in healthcare.

Computer Vision Algorithms

Another area where you can see the benefits of AI in the medical field is in computer vision (CV). When combined with DL algorithms, CV can improve patients’ health by enhancing physicians’ ability to diagnose diseases like breast cancer.

Early detection increases the chances of surviving almost any illness. However, diagnosing breast cancer requires expertise in analyzing ultrasound images and health records. Using CV technology, doctors can improve their ability to detect the disease in three main areas:

• Natural image classification
• Target detection
• Semantics segmentation

Reducing human errors in the diagnosis process has added benefits. It creates parity between rural and urban doctors in terms of their diagnostic abilities. Over time, this will not only save patients’ lives but also increase equity in healthcare.

Reinforcement Learning Algorithms

There are additional benefits of AI in healthcare that can be traced to reinforcement learning (RL). This is a subset of machine learning where models are trained in sequential decision-making. RL is commonly used in game development, where characters must adapt and react to their environments.

These types of learning models can surpass human performance in certain tasks, making them perfect for robotic surgery. They have been successfully used to automate surgical movements with RL-trained scissors. Some have speculated that AI may replace doctors in certain fields as the technology advances. Yet, research indicates that partnerships between physicians and machines will achieve better results than either working alone. 

What Are the Benefits of Generative AI in Healthcare?

There are many benefits to using generative AI in healthcare, including advancing medical research, removing human error and improving patient care. Generative AI takes traditional AI a step further by using data inputs to create content in the form of text, images and code. 

Communication between healthcare providers is crucial when it comes to patient health. Using generative AI, clinicians can automate tasks, removing human error and time constraints when processing health information. Physicians can also use it to create summaries and notes for referrals and follow-up appointments. Additionally, it can be used to create patient recommendations and coordinate care based on medical history and clinical health data.

Generative Adversarial Networks

Generative adversarial networks (GANs) are a form of generative AI that have advanced clinical research, assisted with medical education and improved patient privacy. In essence, GANs can create synthetic data that fill gaps in data sets. 

For example, if a small sample is being used in clinical research, a GAN can create synthetic data to increase the sample size. The larger sample can provide more data for machine learning algorithms to generate insights. This technology is also used to create deep fakes. Deep fakes are artificially generated images, videos or other forms of media, where a person’s likeness or voice is manipulated to appear as if they are doing or saying something that they did not do or say. Deep fakes are a top concern when it comes to the ethical use of medical AI.

Ethical Issues With AI in Healthcare

While there are many benefits of AI in healthcare, many ethical considerations arise when using private health data to program AI models. From data privacy to bias, we need to be prepared to address these issues as we harness the power of AI. 

One of the top ethical concerns of using medical AI is AI bias in healthcare. The training data used to program AI models can contain racial and gender bias. This can lead to biased results, which can perpetuate existing inequities in healthcare.

Privacy issues are another major ethical concern regarding the use of AI in healthcare. Many vendors and providers in the healthcare ecosystem share private patient data. Using this data to train AI models exposes patient information to potential data breaches.

How Will AI Help Healthcare in the Future?

AI will help healthcare in the future by making the medical ecosystem more efficient, assisting physicians in preventing illness and helping discover new medications. AI can assist medical professionals in additional ways. One of the most exciting prospects for the future of AI in healthcare is combining it with other forms of technology, like virtual reality (VR). With this form of technology, physicians can provide virtual consultations and remove some of the geographical barriers some patients face in accessing healthcare services. 

Preventive medicine is another way that AI can have a major impact in the future. Being able to predict illness, provide early diagnoses and analyze prescription information will completely transform how we interact with health systems moving forward.

Learn more about the life-saving benefits of AI in healthcare by following Robert F. Smith on LinkedIn.