The healthcare sector has seen many digital disruptions over the years. Healthcare entities keep a close eye on digital technologies that can change the landscape. When AI first came into the scene, healthcare entities were quick to respond. They used AI for improved patient care, report generation, research, and many other operations. Over the years, new AI-led technologies emerged within the healthcare sector. Among the latest technologies in the healthcare sector, AIOps in healthcare and generative AI are the talks of the town. Many healthcare entities around the world are already using generative AI for improved diagnosis and treatment plans. Read on to understand how generative AI and AIOps are revolutionizing the healthcare sector.
Are you familiar with the concept of generative AI?
Generative AI is a branch of AI that focuses on generating new content. The new content is generated based on the input data. The input data could be unstructured or normal text. With the help of generative AI, the input is converted into different types of content, depending on the requirements. Generative AI can produce imagery, text, audio, and other types of synthetic data. When generative AI first came into the scene in the 60s, it could only generate text data. Chatbots were the first to use generative AI and produce content.
Over the years, advancements in generative AI opened new doors. Generative adversarial networks came into the scene that could produce graphics, videos, and images. The best part of generative AI is that it can produce new content within seconds. Healthcare entities use generative AI to create medical imagery, MRI reports, and X-rays quickly. Since timing is critical in the healthcare sector, generative AI helps increase the speed of generating new content. Many healthcare entities also use generative AI to create custom treatment plans for different patients based on their treatment history. There are other use cases of generative AI in the healthcare sector, from improved drug discovery to health management.
How generative AI helps in the healthcare sector?
Every new-age technology has some benefits in the healthcare sector. Without any benefits, healthcare entities might not invest in new-age technologies. For example, AIOps in healthcare are used for increased service availability of software systems. Here are the benefits of generative AI in the healthcare sector:
1. Advanced drug development
Healthcare entities invest heavily in drug discovery and development. The discovery of a new drug for an existing medical condition can help healthcare entities boost their ROIs. Newly synthesized drugs must be tested on animals or humans before launching into the market. However, there is always a risk involved with drug testing on animals or humans. Generative AI can help identify the right candidates for drug testing based on their bodily functions. It can also run simulated tests on different candidates in synthetic environments. When the chances of success are high, a real test is performed on the selected candidates.
2. Custom treatment plans
A custom treatment plan is prepared according to the medical history of a patient. The bodily functions of a patient are also considered for creating a custom medical plan. However, healthcare entities fail to create custom treatment plans for each patient. Since the healthcare sector deals with many patients daily, it is a challenge to create a personalized treatment plan for each patient. Generative AI can take the medical history, symptoms, and bodily functions of the patient as input to generate custom treatment plans. Healthcare entities do not have to worry about the service availability of a software system that generates treatment plans. Since generative AI can produce custom treatment plans within seconds, there is no need for an external software system.
3. Advanced medical imagery
Traditional medical imaging solutions produce reports after a long period. At the same time, healthcare entities also have to worry about the service availability of the medical imaging solution. If the software solution loses its service reliability, the healthcare entity might fail to produce medical images, x-rays, and MRI reports. With generative AI, the time taken to produce medical images is reduced drastically. Also, generative AI can identify abnormalities in medical images. Doctors and diagnostic experts will be informed of the existing problem by the generative AI system.
4. Improve healthcare initiatives
Generative AI can analyze large amounts of data within seconds. Healthcare entities have to focus on population health management to launch specific services. For example, healthcare entities might deploy new treatment techniques for people in an area known to possess hereditary diseases. Generative AI can analyze demographic information on a granular level and generate rich insights. If healthcare entities are looking to launch healthcare schemes for some areas or unrecognized communities, generative AI is the right choice. Even government healthcare organizations can use generative AI for better population health management. Governments are expected to invest heavily in generative AI for launching effective public healthcare schemes.
The need to regulate generative AI in the healthcare sector
There is a need to regulate generative AI in the healthcare industry. The healthcare sector is all about precision and accuracy. A second delay in service availability can cause a patient his/her life. For the same rationale, healthcare entities must use technologies that always make the right decisions. Many healthcare organizations are still researching the possibilities of generative AI. It is found that generative AI can be biased or opinionated at times.
When generative AI is biased, a healthcare organization might face ethical concerns. Healthcare entities cannot depend on biased software solutions that might pose a risk to patients. However, the issue can be solved by training the generative AI model in the right way. Misuse of generative AI is another ethical concern for a healthcare entity. Employees might think that generative AI can replace their jobs. A healthcare entity must focus on navigating through the ethical concerns of generative AI.
In a nutshell
Whether it is generative AI or AIOps in healthcare, the implementation must be right. You cannot expect untrained generative AI systems to make better decisions. Besides training generative AI models, an organization must also try to eliminate ethical concerns.