How Healthcare is Using Generative AI in 2025

Generative AI in Healthcare uses techniques like generative adversarial networks (GANs) and large language models (LLMs) to identify intricate patterns within vast amounts of data, such as medical images, electronic health records (EHRs) and patient health data. These models can generate new, contextually relevant information, helping medical professionals diagnose and treat patients more effectively.

To give you an idea, the global generative AI market is expected to grow by USD 17.2 billion by 2032, with a CAGR of 37% from 2023 to 2032 [See source]. This growth shows how organisations are adopting generative AI to lead breakthroughs in Healthcare. Generative AI applications such as virtual nursing assistants have the potential to save the healthcare sector USD 20 billion annually, further showing the impact of AI on healthcare.

Source: https://market.us/report/generative-ai-in-healthcare-market/ 

Applications of Generative AI in Healthcare

Here’s how healthcare organisations are using Generative AI solutions to boost efficiency and help their patients:

1. Medical Diagnostics

The first and perhaps most impactful application of generative AI in healthcare is medical diagnostics. By analysing multimodal data such as medical images, clinical notes, and patient histories, generative AI models can assist in identifying diseases and conditions that human clinicians may overlook. For example, AI models can analyse radiological images like X-rays, MRIs, or CT scans to detect abnormalities, such as tumours or fractures, and generate detailed textual findings to assist radiologists in diagnosing conditions more quickly and accurately. 

Systems like AI-Rad Companion use natural language generation (NLG) models to automatically compose radiology reports based on medical images. These reports provide clinicians with a comprehensive overview of potential issues, allowing them to act faster on critical findings. Similarly, a German startup MedaPlus created an AI-assisted software that analyses heart and breathing sounds, identifying abnormalities and alerting doctors to potential issues early on. 

However, AI systems for medical diagnostics require high-performance computing to process large multimodal datasets such as radiological images and clinical notes. NexGen Labs’ GPUaaS solutions provide the scalable, eco-friendly infrastructure needed for deploying these AI models efficiently, with secure and compliant data management to handle sensitive healthcare data.

2. Drug Discovery

Drug discovery has traditionally been a lengthy and expensive process, often taking years to move from research to clinical trials. However, generative AI can accelerate this process by generating new molecular structures based on existing biological and chemical data and suggesting novel drug candidates. This technique allows researchers to quickly explore vast chemical spaces and identify compounds that might have been overlooked.

For example, Insilico, a biotechnology company applied generative AI throughout every stage of the preclinical drug discovery process [See source]. This included identifying potential molecules for drug targeting, generating new drug candidates, assessing how well these candidates would interact with the target and even forecasting the results of clinical trials.

3. Medical Research

Researchers can use AI models to analyse vast datasets, generate hypotheses, or suggest new areas of exploration. These models can process large volumes of scientific literature, identifying patterns and connections that might not be immediately obvious to human researchers. For example, Google Health developed an AI-powered system that integrates into breast cancer screening workflows, helping radiologists detect breast cancer earlier and with consistency comparable to trained professionals [See source]. 

Partner with NexGen Labs for AI Research and Development consulting to explore similar breakthroughs in medical research. By leveraging our latest NVIDIA hardware like the NVIDIA HGX H100, NVIDIA HGX H200 and the latest NVIDIA Blackwell GB200 NVL72/36 and dedicated GPU clusters for AI, researchers can drive innovative projects for faster data analysis, better model training and more accurate insights in fields like healthcare and beyond.

4. Clinical Decision Support

In clinical decision support, generative AI can provide healthcare professionals with data-driven recommendations for treatment plans. These systems analyse patient data—such as medical history, lab results, and imaging and suggest the most appropriate treatments based on the patient’s needs. AI models can also predict patient outcomes, identify potential complications, and help prioritise interventions. 

If your organisation is at the early stages of adopting Generative AI solutions in healthcare then NexGen Labs’ AI Strategy Consulting could be your solution. We offer personalised guidance for organisations new to AI, covering readiness assessments and strategic roadmaps. We will help your organisation to integrate Generative AI into your workflows and leverage NVIDIA infrastructure to process extensive datasets like scientific literature and patient records.

5. Virtual Health Assistants

Generative AI also helps in patient engagement through virtual health assistants. These systems are powered by large language models (LLMs) to interact with patients in natural language, answering questions, providing health information and offering personalised advice. Virtual assistants can help guide patients through treatment journeys and remind them of appointments or mental health support through conversational interfaces.

Security and Privacy Challenges in Generative AI for Healthcare

Healthcare data is highly sensitive and any compromise in its security can have severe consequences, not only for patients but also for healthcare institutions. Generative AI systems are data-hungry and rely on large amounts of patient data to train their models. This data may include personal health information, medical histories and genetic data that, if exposed, could lead to significant privacy violations. And because generative AI models are often seen as “black boxes,” it is challenging to fully understand how they arrive at certain conclusions, which can exacerbate concerns about data misuse and misinterpretation.

One key challenge is the risk of model exploitation by malicious actors. If generative AI models are trained on unencrypted or poorly protected data, they could become vulnerable to attacks. For instance, attackers could reverse-engineer AI models to gain insights into patient information or manipulate the system to generate false or misleading outputs. Therefore, securing AI models against such threats is imperative.

But it does not end here, generative AI models may inadvertently learn and propagate biases present in the training data. In healthcare, this could lead to inaccurate diagnoses, treatment recommendations or even disparities in care for certain patients. Hence, addressing data security concerns is critical in healthcare. NexGen Labs’ secure GPUaaS solutions ensure data protection, with regional compliance and robust processes for secure data transfer and deletion, mitigating risks of privacy violations or exploitation by malicious actors.

NexGen Labs: Your Partner for Generative AI

Partner with NexGen Labs to accelerate your healthcare AI journey, ensuring scalable infrastructure and expert guidance. Speak to an expert to learn about how we can help you implement Generative AI solutions in healthcare.

You May Also Like to Read

• A Guide to Generative AI in the Enterprise

• 10 Most Popular Use Cases of AI in Enterprise in 2025

FAQs

How does Generative AI improve medical diagnostics?

Generative AI models analyse complex data, such as medical images, to detect diseases like tumours and fractures, helping clinicians diagnose faster and more accurately.

What role does Generative AI play in drug discovery?

Generative AI accelerates drug discovery by creating novel molecular structures and predicting the effectiveness of drug candidates, speeding up the development process.

How can Generative AI assist in clinical decision-making?

By analysing patient data, AI models suggest treatment plans, predict outcomes, and prioritise interventions, aiding healthcare professionals in making informed decisions.

What services does NexGen Labs offer for healthcare AI adoption?

NexGen Labs provides AI strategy consulting, high-performance infrastructure, and expert guidance to help healthcare organisations implement generative AI solutions efficiently.

How does NexGen Labs ensure data security in healthcare?

We use secure GPUaaS solutions with robust compliance and secure data management protocols to protect sensitive healthcare data from breaches and exploitation.