- Generative AI models can create synthetic images that are close to real images. Some of the prominent generative AI models used for imaging are DALL-E 2, GLIDE, and ChatGPT.
- In healthcare, generative AI helps doctors to create copies of patient data and automate form-filling tasks. It can also be integrated with EHR for documentation work.
- Application of generative AI in healthcare is to research ideas. ChatGPT, for instance, is a useful tool for idea generation. Users can simply type a desired topic or ask questions and get instant ideas.
- Generative AI in healthcare is used to create synthetic images, videos, and audio. AI-generated content is difficult to distinguish from real images, which can be misleading.
Generative AI is disrupting the fields of art, content, graphic design, research, and journalism. It will change the way creativity is produced in today’s time and the upcoming future. According to Gartner, generative AI is estimated to account for 10 percent of all the data produced by 2025. The maximum of this data is either text or image.
Though generative AI offers a range of benefits such as manuscript writing and generating ideas, it holds concerns for the education sector. The thinking ability of students is likely to get hampered if they rely on generative AI for their assignments and exams.
Nevertheless, with its ability to answer queries, create images, write lengthy text, and help with research, generative AI in healthcare holds great promise for care providers and patients. Even healthcare AI developers can leverage generative AI to create unique features and functionalities that will contribute to better care and outcome.
How Does Generative Artificial Intelligence Work?
Generative AI in healthcare uses ML to process surplus text and visual data. This data is captured from the information fed in the algorithm of the generative AI tool. It creates its output by speculating all the data it has been trained for.
Generative AI responds to prompts in the form of text or images. For example, users can ask for SEO-friendly keywords for solo travel or images of a mountaineer climbing a steep ice wall. Generative AI allows users to ask in-depth questions or a combination of them. For instance, ‘list 10 unique features of telemedicine application.’ The AI then answers within a few seconds.
Applications of Generative AI in Healthcare
1. Improved Medical Imaging
Generative AI models can create synthetic images that are close to real images. Techniques used are GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders). Generative AI is trained on large datasets with multiple disease types, which allows it to synthesize models in any of these disease types. Some of the prominent generative AI models used for imaging are DALL-E 2, GLIDE, and ChatGPT.
2. Reduced Administrative Burden
Documentation is a cumbersome task for the front-desk staff. They have to fill in patient data, schedule appointments, and attend to patient queries. Even healthcare providers have to enter EHR data, which takes a lot of time, and they end up spending less time with their patients. However, with generative AI, doctors can create copies of patient data and automate form-filling tasks. It can also be integrated with EHR for documentation work.
3. Predictive Maintenance of Medical Devices
The role of generative AI in healthcare could be to predict when medical devices are likely to fail. With this knowledge, hospitals and clinics can manage their maintenance and repairs. It results in reduced downtime and increased patient outcomes.
4. Interpreting Unstructured Data
Medical notes, EHR data, and medical images such as X-rays, MRIs, and PET are examples of unstructured data. This type of data creates gaps during analysis, hence it needs to be converted into a structured format. Generative AI can identify unstructured data and convert it. It analyzes data from multiple sources and provides a comprehensive insight to providers.
5. Medical Robots
AI-driven robots are used for surgical operations. It assists in suturing wounds or incisions and provides insights on surgery procedures based on medical data. In healthcare, generative AI can be used to train medical robots for interpreting health conditions.
6. Disease Diagnosis and Screening
Artificial intelligence in healthcare along with predictive analysis helps to identify and diagnose different diseases. It contributes by scrutinizing large data sets and detecting diseases based on the data fed into its system. In the case of generative AI, physicians can use it as a medical knowledge assistant. It can provide a checklist of symptoms for certain diseases, along with a treatment plan.
7. Clinical Trials
In clinical trials, generative AI is used to create synthetic data and enhance datasets. This is the simplest way to increase the size of datasets for clinical trials and research. Also, GANs are used in domain image-to-image translation, such as synthesizing CT images from MRI. These images are later on used as resources to conduct clinical trials.
8. Personalized Medicine
Personalized or precision medicine offers personalized treatment plans to patients. Generative AI can be trained on large medical datasets to create personalized medicine. It also helps physicians to make informed decisions about treatment options.
9. Drug Discovery
In healthcare, generative AI models like generative adversarial networks and variational autoencoders, are useful for the computational creation of new molecules, which help in innovating novel drugs. This process is possible due to the virtual synthesis of images, text, image captions, and speech by generative AI.
10. Population Health Management
ChatGPT, a generative AI can transform population health management. This AI technology can quickly analyze patient data and compare it with other population health data available, and generate in-depth insights to help physicians manage population health.
11. Offering 24/7 Medical Access
As per an article published in the AHCJ, generative AI can offer 24/7 medical assistance by linking it with wearables. It can also remind patients who are due for prescription refills and preventive screenings. Generative AI, like ChatGPT, can respond to medical questions asked by patients, just like Google. But, this chatbot isn’t a clinical decision-making tool, hence it needs human insight too.
12. Clinical Decision-Making
AI along with predictive analytics assists physicians in clinical decision-making. Generative AI in healthcare has access to large datasets and it responds to prompts based on the data fed into it. It is capable of extracting information and providing answers. So, it can be used for clinical decision-making, however, more research is needed for this AI application in healthcare.
13. Generating New Research Ideas
Another application of generative AI in healthcare is to research ideas. ChatGPT in healthcare, for instance, is a useful tool for idea generation. Users can simply type a desired topic or ask questions and get instant ideas. For example, a user can ask ‘which drugs have higher chances of curing varicose veins.’
14. Preventing Medical Errors
Medical errors are a leading cause of death in the USA. Generative AI is capable of correcting mistakes during documentation work. It automatically corrects spellings (which is helpful for e-prescription) and ensures that the right data is filled in the system.
15. Engaging Patients in Conversation
Patient engagement is a vital aspect for healthcare facilities as a constant connection with patients regarding their fitness, medication, and health issues accelerates care delivery. Additionally, it also boosts providers’ practice in a virtual care setting. With the help of generative AI, healthcare facilities can boost patient engagement as it allows users to start conversations with the AI. They can either ask questions related to their health issues or just have a chat about wellness.
Ethical Concerns of Using Generative AI in Healthcare
Generative AI in healthcare is used to create synthetic images, videos, and audio. AI-generated content is difficult to distinguish from real images, posing ethical complications. It can mislead and manipulate real healthcare data. Fake images or videos can be used to defame people and even harass them.
Further, patients use generative AI tools to ask questions, converse, and know more about their medical conditions. So, users of generative AI technology need to assess the accuracy and truthfulness of the generated information because AI may find it difficult to keep up with the latest data. Incorrect information can mislead patients, which can harm their health.
Major Challenges of Generative AI
The foremost challenge faced by generative AI is the privacy and security of ePHI. Generative AI can analyze data, give prompt answers, and ease cumbersome patient documentation work. It has access to crucial patient data during documentation and it stores all questions asked to it. Hence, the privacy and security of patient data are a major concern and a challenge.
Furthermore, generative AI is vulnerable to discrimination and bias, especially if they’re trained on care data that is not a representative of the population it’s meant to serve. This results in inaccurate or unfair diagnoses and treatment. Additionally, providers may lose their ability to make independent judgments if they rely heavily on generative AI.
Generative AI is predicted to expand heavily in the healthcare sector. It will transform the way healthcare is perceived by patients and providers. With its potential to generate images, text, audio, and much more, its applications will not be limited to just the ones stated in this article. It will continue to augment.
Be a part of this AI transformation in healthcare and leverage innovative AI technology for your care facilities. Arkenea, a healthcare software development company, provides a range of AI technologies for healthcare such as robotic process automation, chatbots, predictive modeling, and much more. Arkenea offers best-in-class AI technology that suits your organization’s requirements. Get in touch to know more.