Generative AI in Healthcare | Saxon AI
Healthcare / June, 23 2023

9 Innovative Use Cases of Generative AI in Healthcare 

The global healthcare scenario is grappling with mounting challenges, from pandemics to chronic diseases, mental health concerns, a rapidly aging population, and much more. The already strained healthcare system strives hard to meet the growing demand for quality care. However, generative AI emerges as a ray of hope amidst these complex parameters. With a staggering market opportunity valued at $6 trillion, estimated by Morgan Stanley, this technology’s transformative potential cannot be ignored. Generative AI is not just a fleeting trend. It represents a rapidly evolving ecosystem of tools that can revolutionize healthcare in unprecedented ways. While Goldman Sachs project it to boost global GDP by 7% over a decade, its applications extend far beyond economic gains alone. The boundless possibilities of generative AI are also shaping the future of healthcare. 

What is Generative AI?

Generative AI, powered by large language models (LLMs), has emerged as a groundbreaking technology capable of creating diverse content ranging from text to images, videos, audio, and 3D models. Its ability to generate new and unstructured outputs sets it apart from previous forms of AI and analytics.  

In healthcare, this technology holds immense potential for automating and enhancing manual processes to improve customer experience and boost employee productivity. With the development of healthcare-specific large language models like Med-PaLM, BioGPT, ClinicalBERT, and GatorTron, accurate answers to medical questions can be provided within the healthcare domain. 

As we delve into the top emerging use cases of these tools in the healthcare industry, we also explore whether generative AI is merely a tech hype or actually a game-changing opportunity with transformative implications. 

9 Use Cases of Generative AI in the Healthcare Industry 

Here are nine emerging applications of generative AI in healthcare: 

Drug discovery 

Many medications take decades to develop due to the lengthy and expensive traditional drug discovery procedure. By developing new drug molecules having the potential to be turned into new drugs, generative AI can expedite the process. Pharmaceutical scientists can simply use generative AI to accelerate the drug discovery process. You may wonder how exactly? 

  • By learning from a large dataset of chemical structures and their attributes, the program can generate new molecules analogous to the already existing drugs. Scientists can then test these new molecules in the lab and evaluate their potential as new drugs. 
  • Identifying possible drug candidates and their efficacy and safety testing are key steps in the time-consuming and expensive drug discovery process. Generative AI can accelerate the process by identifying possible drug candidates based on a large dataset of compounds and their features. 
  • Another use case of generative AI in drug discovery is developing virtual compounds. AI algorithms can create virtual compounds and examine them in silico (a computer simulation instead of a laboratory). As a result, this drastically reduces the time and expenses involved in discovering new drugs. 
  • Scientists can use generative AI to design new molecules to discover new drugs. The algorithm can learn from a vast repository of chemical structures and their properties. It can then create new molecules tailored for a particular target. 

Diagnosis of diseases 

Generative AI can revolutionize disease diagnosis by leveraging large datasets of medical images to identify patterns associated with specific conditions. For instance, dermatologists can apply this method to diagnose skin cancer. The program can examine a large dataset of skin images and detect patterns suggestive of skin cancer. As a result, the doctor can make better and more precise diagnoses faster and enhance the outcome. 

Similarly, generative AI can also speed up the diagnosis of diseases by studying medical images, such as CT scans, X-rays, and MRIs. The algorithm can recognize patterns linked to particular diseases by learning from a sizable dataset of medical images. Similar to identifying skin cancer, the algorithm can recognize patterns suggestive of lung cancer, learning from a large CT scan dataset. 

Personalized medical Chatbots 

Healthcare organizations can develop medical chatbots that provide patients personalized medical advice and recommendations. For example, Babylon Health has developed a chatbot that uses generative AI to ask patients about their symptoms and deliver personalized medical advice. 

Patient care 

Generative AI can be harnessed to create personalized treatment plans for patients. The algorithm can examine a patient’s medical background, genetic information, lifestyle choices, and other factors to develop a tailored plan. For instance, the algorithm can examine a patient’s tumor DNA and pinpoint the genetic mutations causing the cancer. It can then suggest a personalized, precise treatment strategy tackling particular genetic mutations. Furthermore, generative AI can augment doctors and healthcare providers to predict patient outcomes. Learning from large datasets of patients, it can uncover patterns linked to specific outcomes and help make informed decisions. 

Medical imaging 

Key elements of patient treatment include medical imaging such as MRIs, CT scans, and PET scans, which help swiftly pinpoint serious injuries and diagnoses. Generative AI can augment healthcare professionals here with faster answers and streamline the imaging process. Moreover, generative AI techniques have the ability to reduce image noise. Coupled with machine learning, it can shorten scan times too. It can automatically detect abnormalities in patient scans without human intervention. The promising outcome of these advanced capabilities is faster patient care, a critical touchpoint when time is short. 

Medical Research 

Scientists can use generative AI to speed up research in medicine. An enormous dataset of scientific literature can be used to train the algorithm, which can then find patterns related to certain study areas. This can assist researchers in coming up with fresh research questions and theories.  

For instance, researchers can find new pharmacological targets using generative AI. The algorithm may find genes and proteins linked to particular diseases by studying a sizable dataset of scientific literature. This can assist researchers in finding fresh pharmacological targets and creating new medications.   

Personalized treatment plans

Generative artificial intelligence can create customized treatment programs by examining vast quantities of patient data and producing treatment recommendations based on that data. For instance, Mayo Clinic researchers have developed a deep learning algorithm that can forecast the likelihood of complications following surgery. It can produce customized treatment recommendations depending on that risk. 

Medical simulation 

Healthcare professionals can implement generative AI to produce medical simulations that can assist in hands-on training. In fact, the University of Michigan researchers have created a generative AI program that can simulate several situations for treating sepsis. This condition can be fatal and is brought on by infection.   

Overall, by enhancing the precision and speed of diagnosis, hastening the discovery of new drugs, and enabling personalized treatment plans, generative AI has the potential to change healthcare in a variety of ways. 

Clinical documentation 

Nuance Communications, reported Microsoft intends to support healthcare professionals leveraging generative AI with its Dragon Ambient eXperience (DAX), which eases clinical documentation by capturing and summarizing physician-patient consultations. This directly consolidates the documentation by recording information, creating electronic health records, and simplifying complex medical language for patients’ understanding. Additionally, Microsoft’s collaboration with electronic health vendor Epic further integrates generative AI to improve message drafting. It enhances the overall efficiency of health systems. 

Challenges in Generative AI for Healthcare 

Generative AI has great potential in healthcare, but there are certain challenges that must be addressed.  

  • Interpretability and trust: Sometimes, the content generated can be challenging to interpret. The difficulties in understanding the algorithm’s decision-making process will impact trust and decision-making.  
  • Acquiring large datasets for training may be challenging, limiting effectiveness in certain healthcare areas.  
  • Ensuring transparency is crucial to addressing biases and inaccuracies, fostering trust among providers and patients.  
  • Ethical concerns arise regarding privacy, security, and algorithmic bias, necessitating careful consideration to avoid disparities in healthcare outcomes. 

Conclusion 

Drug development, disease diagnosis, patient care, medical imaging, and medical research are just a few of the possible applications of generative AI in the healthcare industry. The advantages of generative AI in healthcare are substantial, even though some issues must be resolved. We anticipate seeing more generative AI applications in healthcare as AI technology develops, revolutionizing patient care and enhancing health outcomes. 

Not just healthcare but all other sectors will usher into a new era of innovation with generative AI. Those willing to experiment and harness the power of this AI technology will open up new innovative prospects for everyone. If you want to analyze the opportunities to make the best use of generative AI for your organization, you can register for our unique InnovAIte workshop right away. 

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