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Generative AI is greatly impacting the healthcare industry. A report from Menlo Ventures shows a sharp rise in AI adoption among healthcare providers and payers in 2025.
Meanwhile, the National Academies of Sciences recently emphasised that using generative AI in medicine will require cross-sector collaboration, tight oversight and risk management.
According to a McKinsey report, Generative AI could add up to $4.4 trillion annually to the global economy.
A 2023 study in Nature Medicine found that AI models could diagnose diseases with accuracy comparable to human doctors. Businesses can benefit from Generative AI through increased productivity, personalized customer experiences, and the creation of novel products and services. Early adoption of this technology can provide a competitive advantage.
With its advanced abilities, Gen AI is improving diagnostics, treatment plans, and drug discovery. Artificial intelligence is leading to more personalized and efficient healthcare, better resource use, and boosted patient outcomes.
Healthcare providers and life sciences researchers are turning to Gen AI to dig up new insights from large datasets.
This article explains how AI is being used in healthcare, what is new and what to watch, and where challenges remain.
Healthcare systems are sitting on a mountain of data, but putting all that information to good use is still a challenge. This is where generative AI is starting to make a real difference.
It is being leveraged by 78% of companies worldwide, including the healthcare industry, to find something meaningful from the data.
That's why tools like Delphi-2M are evolving as they can predict a person’s risk for more than 1,000 different diseases by analysing information about their health history and lifestyle. Even better, it can make these forecasts.
Meanwhile, major players in health-care are refining how they use generative AI. Johnson & Johnson, for example, moved from supporting hundreds of generative-AI projects to focusing on a small set of high-value initiatives (such as in drug research and supply-chain) after discovering many pilots did not produce meaningful results.
These two developments point to a more mature phase:
Generative AI is beyond early hype.
Organisations are now testing real-world applications under practical constraints.
Rather than list every application, here are three areas showing particularly strong traction:
Tools like Delphi-2M show how generative AI can help make meaningful predictions. Healthcare experts transition to the world where they will be able to take preventive action before the illness appears.
The most popular use cases of generative AI is clinical documentation. Large language models (LLMs) are being integrated into clinical documentation systems. The reason is the speed at which they can extract information is impossible to humans.
There are countless case studies available on the internet on how generative help healthcare professionals save many hours everyday in the documentation.
As generative AI becomes part of healthcare systems, experts are raising important questions about data bias, reliability, transparency and patient safety. They are calling for clear frameworks to make sure generative AI is used in a responsible and trustworthy way.
Trust: Several studies and reports show clinicians perceive doctors who rely heavily on AI with significant scepticism.
Regulation: Existing medical-device frameworks struggle to keep up with generative models.
Reliability and bias: Synthetic data or outputs generated by AI may suffer from gaps in representativeness, lack of transparency or hidden biases, limiting uptake in clinical settings.
Implementation complexity: Many early projects fail to scale or move beyond pilot stage, as shown by the J&J case above.
If you work in a hospital, life-sciences company or healthcare development company, here are some recommendations:
Focus on business-value applications first rather than broad “innovation” efforts.
Engage clinicians early so any AI solution fits into actual workflows and earns trust.
Design for transparency and reliability, ensuring users understand limitations of generative outputs.
Look at governance and risk as foundational (not optional).
Pilots must link to integration, training, monitoring and support to move into production.
Generative AI in healthcare is moving from experimental to operational. As models become more capable and trusted, healthcare providers, researchers and technology firms will gain an edge if they move thoughtfully.
The Delfi-2M which we have talked about earlier developed by researchers from European Molecular Biology Laboratory (EMBL), German Cancer Research Centre and University of Copenhagen developed can estimate the likelihood of developing diseases such as certain types of cancer, heart disease, diabetes, and sepsis/blood poisoning.
Though it has not been approved yet for clinical use, this example shows that in the coming future, generative AI will help healthcare professionals create a kind of healthcare ecosystem in which they would be able to predict diseases before they emerge.
The NHS in England runs a trial where they used AI in 30 breast-screening clinics to read mammogram images and help detect cancer earlier. Normally, two radiologists check each scan, but with AI, one radiologist can manage the workload of two.
AI enables faster screenings for healthcare professionals. The main concern is making sure the AI doesn’t produce too many false positives.
In Tamil Nadu in India, government hospitals test AI tools to read X-rays and CT scans. This shows, in rural and smaller hospitals when there are not enough specialists, AI can be put to use. The AI acts like an extra pair of expert eyes in rural areas.
However, human doctors still make the final decision, and for this system to work well, hospitals need proper integration and staff need to be trained to use it effectively.
Academic research shows that generative AI can help clinicians convert clinician-patient interactions into notes (SOAP/BIRP), reducing administrative burden.
Still, accuracy is a key concern with AI, as many times it hallucinates information even though it is not being present in the source.
Johnson & Johnson reviewed its generative AI portfolio and found only 10–15% of use cases delivered ~80% of value, prompting a pivot to focus on drug-discovery, supply-chain and internal support tools. This means, organisations must be selective rather than scattergun in AI deployment.
Media reports describe instances where large language-model chatbots provided patients or their families with insights that prompted further investigation or specialist referral.
Source: Wired
These data shows that generative AI acts as a supplementary tool for pre-consultation or triage. But, it is not a replacement for clinical judgment.
Recent research argues that instead of direct therapy delivery, generative AI may have higher impact when used to train mental-health professionals or peer-support personnel. This is a promising use case of generative AI because it addresses workforce shortages in mental-health service delivery.
Healthcare organisations are applying AI to manage bed occupancy, staff rostering, equipment maintenance and scheduling. Technically, these are not medical tasks, but vital for smooth operations.
AI helps balance workloads, understand demand, resulting in smooth operations. The challenge is that many hospitals are still using outdated systems, so AI integration has remained a challenge for many.
Another use case of AI is creating synthetic data, such as patient records, scans, and simulated treatment outcomes. This lets them train and test AI models without using real patient information.
It also allows scientists to explore “what-if” scenarios or test tools before real-world use. The big challenge is ensuring this synthetic data feels realistic and captures the diversity found in actual patients.
Generative AI technology has astounding capabilities when it comes to extracting, summarising from information datasets. It can quickly read and then summarise scientific papers, helping doctors and researchers stay updated without spending hours on literature reviews.
It’s a useful way to scan new studies and spot important findings in a busy life. But these AI summaries can sometimes miss details, so they should be treated as a starting point rather than a replacement for reading the full paper.
Although still emerging, startups are deploying generative-AI-powered chatbots that handle initial patient triage, follow-up questions and basic education, then escalate to human clinicians when needed. However, it’s important that patients know when they’re talking to an AI and when a human professional is involved.
Generative AI is increasingly used to interpret clinical notes and assign correct billing codes and procedure classifications, a big administrative pain point in healthcare. Still, every AI-generated code needs careful review.
The shift in J&J’s AI strategy (see use case 5) highlights how generative AI is applied not just clinically but across supply-chain, manufacturing and internal support in healthcare organisations.
While breast-cancer screening (see use case 2) is one example, AI systems are also being trialled for broader imaging applications—detecting signs of lung disease, cardiovascular issues and rare conditions.
Research demonstrates generative-AI chat interfaces can support patients in tracking risk factors (for example in COVID-19 severity) by conversationally analysing data points and providing personalised insights.
The future of healthcare is here, and it's powered by generative AI. From revolutionizing drug discovery to streamlining administrative tasks and personalizing patient care, generative AI is poised to transform the way we approach health and wellness.
As we've explored, the potential applications of this technology are vast and continue to expand.
Embracing this transformation is not just an option, but a necessity for healthcare providers, researchers, and organizations that want to stay at the forefront of innovation.
Are you ready to unlock the full potential of generative AI in your healthcare initiatives? Don't get left behind. Contact us today for generative AI development services and let's shape the future of healthcare together.
Generative AI is a type of artificial intelligence that creates new content, such as text, images, or code, based on patterns it learns from existing data. In healthcare, it can be used for various tasks, including generating medical reports, designing treatment plans, creating synthetic medical images for research, and even developing new drugs.
Generative AI enhances healthcare outcomes by enabling faster and more accurate diagnoses, personalizing treatment plans, predicting patient responses to therapy, and automating time-consuming tasks, allowing healthcare professionals to focus on direct patient care.
Generative AI is used to analyze medical images to detect early signs of disease, create realistic simulations for surgical training, generate personalized summaries of patient medical records, and even design new molecules with potential therapeutic benefits.
Yes, ethical concerns include the potential for bias in AI algorithms, ensuring the transparency and explainability of AI-generated results, and protecting patient privacy when using sensitive medical data. It's essential to address these concerns through careful development, validation, and regulation of AI systems.
The future of generative AI in healthcare is promising. As technology advances, we can expect even more sophisticated applications, such as predicting disease outbreaks, designing personalized medicine, and creating virtual patient avatars for clinical trials. However, it's crucial to proceed responsibly, addressing ethical and privacy concerns while maximizing the potential benefits for patients and healthcare providers alike.
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