Generative AI Healthcare Industry: Benefits & Challenges in 2024

Generative AI has the potential to transform healthcare industry by providing doctors and other healthcare providers with powerful tools for analyzing medical data and making more accurate diagnoses and personalized treatment plans. This renders generative AI healthcare an important area of improvement.

This article explains the current state of generative AI in healthcare, its potential benefits and challenges, and discusses the future direction of this rapidly-evolving field.

How does generative AI work?

Generative AI, also known as generative artificial intelligence, is a type of AI that is focused on generating new content or to create synthetic data in the form of text, images, or other forms of media. Generative AI algorithms use deep learning techniques/machine learning models to learn from large amounts of data and generate new content similar to the input data.

How can generative AI be used in the healthcare industry?

In the context of healthcare, generative AI can be used to:

• Create new medical images, such as X-rays or MRIs 
• Generate personalized treatment plans based on a patient’s medical history and other factors. 

These 2 use cases can generate substantial benefits in healthcare.

What are the benefits of using generative AI in healthcare?

1. Streamlined drug discovery and development: Generative AI can help speed up the process of drug discovery and development by identifying potential drug candidates and testing their effectiveness in silico (i.e. using computer simulations) before moving on to the clinical trials on animals and humans.

2. Personalized medicine: Generative AI algorithms can potentially help create personalized treatment plans for patients by taking into account their medical history, symptoms, and other factors. However, this is a hypothetical benefit of generative AI and we have not yet seen a real-life case study of this yet.

3. Improved medical imaging: Generative AI can help improve the accuracy and efficiency of using machine learning in combination with medical imaging techniques, such as CT and MRI scans. Machine learning models can automatically identify abnormalities in images and alert doctors to potential issues.

Figure 1. 14-hour-long progressive outputs of a generative AI model to produce fake chest X-rays. (Source: Future Healthcare Journal)¹

Generative AI improves machine learning effectiveness via these steps:

• A generative AI algorithm like generative adversarial networks (GANs) is trained on real patient data
• The generative AI model generates new images that are similar to the input data (as can be seen in Figure 1) but can result in better machine learning due to increased volume or diversity in the data
• AI model is trained on the synthetic data

4. Population Health Management: Using generative AI healthcare can also lead to better population-level health management by allowing policymakers to

•  Access more detailed demographic information 
•  Design targeted public health initiatives that benefit underserved communities.

What are some potential challenges of generative AI healthcare?

While there are many potential benefits to using generative AI in healthcare, there are also some possible  challenges and drawbacks. Some examples include:

1. Privacy and security: Patient privacy is strictly regulated. The use of generative AI in healthcare also raises concerns about protecting patient privacy, sensitive medical data and the potential for misuse or unauthorized access to the healthcare data.
2. Bias and discrimination: Generative AI algorithms can be prone to bias and discrimination, especially if they are trained on healthcare data that is not representative of the population they are intended to serve. This can result in unfair or inaccurate medical diagnoses or treatment plans for underprivileged groups such as women or non-white races.
3. Misuse and over-reliance: If generative AI algorithms are not used properly, they can lead to incorrect or harmful medical decisions. In addition, there is a risk that healthcare providers may become overly reliant on these algorithms and lose the ability to make independent judgments.
4. Ethical considerations: The use of generative AI in healthcare raises several ethical concerns, such as the potential impact on employment in the healthcare sector.

The future of generative AI and its impact on healthcare 

The future of generative AI in healthcare is likely to be very significant as the technology continuously advances and becomes more widely adopted. Some potential future developments include:

1. More sophisticated algorithms: Machine learning algorithms are likely to become more refined  over time, with an enhanced  ability to analyze large amounts of healthcare data and identify patterns and trends. This will enable healthcare providers to make more accurate and personalized diagnoses and treatment plans.
2. Broader applications: Generative AI is likely to be used in a wider range of healthcare settings and for a more comprehensive range of applications, such as predictive modeling of disease outbreaks and drug discovery.
3. Better integration with other technologies: Generative AI will likely be integrated with other technologies(e.g., medical imaging and wearable health devices) to provide more comprehensive and personalized patient care.
4. Increased collaboration: Collaboration between healthcare providers, researchers, and technology companies to develop and implement generative AI algorithms in healthcare settings are expected to increase.

If you want to learn more about how AI technology is implemented in healthcare, you can check our other articles:

• Top 18 AI Use Cases in Healthcare Industry
• Top 6 Challenges of AI in Healthcare & How to Overcome Them
• Synthetic Data for Healthcare: Benefits & Case Studies in 2022
• 12 Deep Learning Use Cases / Applications in Healthcare in 2022

If you have questions on the use of generative AI in healthcare, don’t hesitate to contact us:

External Links

• 1. “Generative adversarial networks and synthetic patient data: current challenges and future perspectives.” NCBI. Accessed 18 December 2022.