5 Use Cases for Gen AI in Healthcare: benefits, challenges and the future


The Healthcare industry is one of our most crucial. It has always gone hand-in-hand with innovation- fighting the war on diseases, infections and other ailments. However, many healthcare organisations are plagued with legacy issues from failing to innovate in back-end technologies and committing to digital transformation. The inefficiencies this creates are felt right across the industry and are holding healthcare back. Gen AI has burst onto the scene in recent years, transforming other industries, like manufacturing for the better- less waste, better allocation of resources, etc. So, can the same be done in healthcare? In this blog, we have assembled top use cases for Gen Ai in healthcare.


Use Cases for Gen AI in Healthcare

Our 5 top Gen AI use cases for healthcare:

  1. Medical research and data analysis
  2. Automating admin tasks
  3. Medical imaging
  4. Predictive Healthcare
  5. Resource Management


Medical research and data analysis

Generative AI can efficiently process large volumes of medical data, automating tasks like data extraction and document review, thus freeing up researchers’ time for more critical tasks. Additionally, Generative AI aids in medical document summarisation, providing concise overviews that accelerate comprehension and decision-making, particularly when dealing with extensive literature. Trend analysis is also facilitated, as Generative AI detects patterns and trends within vast datasets, ensuring researchers stay up to date with the latest advancements.


Moreover, Generative AI assists in overcoming challenges related to data integration from diverse healthcare sources, offering a unified understanding of medical information. It also optimises resource utilisation by automating tasks and maximising available resources, benefiting projects with limited funding or access to high-performance computing.


Automating administration tasks

Administration tasks in healthcare need to be exact and rigorous, however, this makes them slow and inefficient. Here lies another use case for Generative AI due to its potential to streamline operations, optimise resource allocation, and enhance decision-making processes. Generative AI can automate repetitive administrative tasks such as:


  • Appointment scheduling: Optimising time slots and automating the booking process.
  • Documents and records: Automating data input via document extraction, forming medical notes and even automatically updating patient records, thus reducing the manual effort required from healthcare professionals.
  • Procurement and invoicing: AI is capable of automating the processing or procurement, invoicing and various other financial documents- enhancing precision and removing human error, thus enabling better allocation of financial resources and stronger relationships with suppliers.
  • Data extraction and inputs: Gen AI is capable of intelligent document extraction and via pre-set rules, can input this to various resources. Furthermore, it can also detect when there are errors with the data it extracts and sends amendment requests via email. This minimises the manual work required, speeding up this process for reallocation of staff to projects of greater importance. It also has the potential to increase data quality within a healthcare database.
  • Patient communications: Gen AI language models can be used as chatbots to communicate with patients for routine inquiries, appointment booking and reminders. This enables to staff that would ordinarily perform these tasks to be reallocated to areas of greater importance.
  • Process optimisation: Gen AI can analyse administrative processes and suggest areas of optimisation, coupled with the aforementioned suggestions, processes can be streamlined for maximum efficiency.

Medical imaging

Medical imaging, such as X-rays, CT Scans and so on, can be read easily and analysed by trained Generative AI models. This can accelerate the processing of medical images by automating tasks such as image segmentation, feature extraction and anomaly detection. This streamlines the diagnostic workflow and enables healthcare professionals to focus on interpreting results and making treatment decisions. In short, it can create a summary of what’s right or wrong, from which a Doctor validates the findings.

Predictive Healthcare

Predictive Healthcare, powered by Generative AI, harnesses pre-trained models to analyse comprehensive patient health records, encompassing genetic information, medical history, lifestyle factors, and more. By leveraging this wealth of data, Generative AI swiftly and efficiently identifies individuals at risk of developing specific medical conditions, such as diabetes, cardiovascular diseases, or cancer. This predictive capability enables healthcare providers to proactively intervene with targeted preventive measures, personalised treatment plans, and lifestyle interventions, ultimately mitigating disease progression and improving patient outcomes. Generative AI also facilitates population-level risk stratification, allowing healthcare systems to allocate resources effectively, prioritise interventions, and implement preventive health initiatives on a large scale.


Through predictive healthcare, the healthcare landscape can shift from reactive to preventive medicine paradigms.


Resource management

Healthcare organisations are often caught between the scenario of an inventory oversupply met with a sudden shortage. The recent PPE crisis during the COVID-19 pandemic was an example of this and has brought home the significance of this cycle. Furthermore, poor patient flows and the lack of resources to handle them leads to unnecessary wait times in admission and also in being moved to other areas of care. All of which needlessly up the cost of treatment as well as putting patients at risk of hospital-acquired complications. With that in mind, here are some of the use cases where Gen AI can transform these inefficiencies:


Demand Forecasting: Generative AI models can analyse historical data to predict future healthcare demands, such as patient admissions, procedure volumes, and medication needs. By forecasting demand, healthcare organisations can optimise resource allocation, staffing levels, and inventory management to meet patient needs efficiently.


Facility Optimisation: Generative AI can assist in optimising the use of healthcare facilities, including hospitals, clinics, and waiting rooms. By analysing patient flow patterns, appointment scheduling, and resource utilisation data, these models can identify opportunities to streamline operations, reduce wait times, and maximise facility capacity.


Inventory Management: Generative AI algorithms can optimise inventory levels of medical supplies, pharmaceuticals, and equipment based on demand forecasts, usage patterns, and expiration dates. This helps healthcare organisations minimise waste, reduce stockouts, and ensure the timely availability of essential resources for patient care.



The risks of Gen Ai in Healthcare

The good rarely comes without the bad and the ugly. Gen Ai is no expectation of that. Despite having near-miraculous potential for healthcare, there are still some limitations attached. Here are our top risks to be aware of.


Time-dependent knowledge

Gen AI is only as smart as its last update. It needs regular updates and training on the latest advancements to produce the most accurate outputs. This creates complications for the deployment as regular update procedures need to be developed to get the most from it.


The medical world can change fast, so It is crucial that a Gen AI program would have access to the latest updates. The risks attached to not doing so could lead to erroneous results, inefficient suggestions or even mis-prescribed patient care.



Lacking in explanation

Gen AI models are great for prediction but not always articulating it. Without clear explanations of how these models operate and make decisions, it becomes challenging to ensure their reliability, fairness, and effectiveness in clinical practice. This can erode trust among healthcare professionals and regulatory bodies and jeopardise patient safety. Enhancing its ability to explain and transparency in Generative AI models is paramount to address these concerns and foster trust.


Computational power to run algorithms.

Healthcare datasets are often massive and complex, requiring significant computational resources for training and inference. Generative AI models, especially those based on deep learning architectures like GANs (Generative Adversarial Networks) or VAEs (Variational Autoencoders), demand substantial computational power to process these datasets effectively. Secondly, real-time applications in healthcare, such as medical image analysis or patient monitoring, necessitate rapid inference times, which may be challenging to achieve without sufficient computational resources.

Controlled usage of programs

As we have mentioned, Gen AI is lacking when it comes to explaining itself. It’s also not an authority to make medical decisions and requires clinical verification. When assisting with forming tailored care routines it should only be accessed by those with the authority and experience to make a medical decision- i.e. Doctors and medical staff. The risk of giving patients access to unverified information is they may take the wrong course of action or worse, panic, should the AI produce extreme or erroneous results. Further still, Gen Ai lacks the humility and empathy required to deliver, what may be difficult news in an appropriate way.


The Future of Gen AI in Healthcare

As we have covered in this blog, the potential of Gen AI in healthcare is revolutionary. Whilst there are naturally some concerns, the impact that the industry will experience is hugely positive. Faster treatments, smarter resource allocation and more data for research- Gen AI is the natural dovetail for many of the main issues currently plaguing the healthcare industry. Whereas the future of healthcare may be data-driven, the underpinning theme is that it will become more human-centric, considering our needs and weaknesses, and building on them to advance the whole industry, no matter which bit you touch.


For more information on what the human impact of the Gen AI revolution will be, click here.