July 25, 2024 - In our Q1 2024 survey, more than 70 percent of respondents from healthcare organizations—including payers, providers, and healthcare services and technology (HST) groups—say that they are pursuing or have already implemented gen AI capabilities ...

March 26, 2025 - To better understand how US healthcare leaders are thinking about gen AI use cases, McKinsey launched a research effort to gather insights from leaders in payer organizations, health systems, and healthcare services and technology (HST) groups. We surveyed stakeholders about their plans for generative AI solutions, including their level of implementation, their plans for adoption, the anticipated benefits, and ROI expectations.

In recent years, natural language processing and machine learning have found applications in various health care use cases, but new Generative AI models are taking health care technology to new heights. These models demonstrate unprecedented capabilities in natural language generation, summarization, translation, insight retrieval, reasoning, and managing unstructured, unlabeled data.

March 15, 2024 - Another unique capability is that they can be used to perform unsupervised learning, which means that they can learn from data without explicit labels [8]. This can be useful in situations where labelled data is scarce or expensive to obtain. Furthermore, generative AI models can generate synthetic data by learning the underlying data distributions from real data and then generating new data that is statistically similar to the real data.

The generation of synthetic data opens new avenues for model training for diseases and simulation, enhancing research capabilities and improving predictive accuracy. In non-clinical contexts, Gen AI improves medical education, public relations, revenue cycle management, healthcare marketing etc.

3 weeks ago - Generative AI has shown its ... According to McKinsey, GenAI can summarize large amounts of data from patient histories, freeing up healthcare professionals to focus on more complex patient needs....

Generative AI models like generative adversarial networks (GANs) and large language models · (LLMs) are used to generate various data modalities including text and image data, which are then used for various scenarios including drug · discovery, medical diagnosis, clinical documentation, patient education, personalized medicine, healthcare administration and medical education

Other advanced statistical and machine learning techniques attempt to balance data utility and privacy. Each method has its strengths and limitations, and the choice depends on the specific requirements of the health care application and the sensitivity of the data involved. The applications and challenges of generative AI in health care, including privacy issues and AI-human collaboration, are explored by Fui-Hoon et al [48]. They discuss several privacy issues related to generative AI, such as the potential disclosure of sensitive or private information by generative AI systems, the widening of the digital divide, and the collection of personal and organizational data by these systems, which raises concerns about security and confidentiality.

July 7, 2025 - Generative models can augment training datasets for diagnostic algorithms without risking patient confidentiality by generating realistic synthetic data that retains the statistical properties of real patient data.

According to statistics, the cost of new drug development, including failed drugs, averages between 1 billion and 2 billion USD, and Gen AI can help the pharmaceutical industry save billions annually.

2 weeks ago - Learn how to use Amazon Nova and the newly released RAG evaluation feature for Amazon Bedrock Knowledge Bases, to assess how well healthcare RAG applications retrieve and use medical information to generate accurate, contextually appropriate responses. Read the blog » · Learn how modern approaches, notably using Generative AI technology and modern data services can help us explore Real World Data (RWD) such as electronic health records or health insurance claims to simplify RWE generation.

Clinical documentation. Generative AI can address a range of needs related to clinical documentation. Today, models can draft clinical notes in specified formats using a transcription of doctor-patient interactions. Researchers are also developing models that could compile and verify information in electronic health records to obtain insurance preauthorization.

1 month ago - Everson and colleagues, in “Uptake ... of generative artificial intelligence (AI) adoption, one-third of hospitals reported already using generative AI, with another 25%......

January 31, 2024 - This content creation capability, coupled with the ease of use and accessibility provided through user-friendly interfaces, has led to a surge in its adoption and use by many professionals, including health care providers. The overreliance on digital information sources traditionally stemmed from patients seeking to better understand their conditions. Now, with generative AI, health care providers might also lean heavily on AI-assisted decision-making.

Chat with a 50 million patient population dataset to get instant insights, generate reports, and visualize trends with AI-powered analytics. ... GenHealth.ai is building AI for healthcare from the ground up.

November 21, 2025 - Our survey reveals that 85% of all generative AI spend in healthcare currently flows to startups rather than incumbents. Even in spaces like ambient scribing where Microsoft’s Nuance had deployed DAX medical speech recognition solutions to ...

May 30, 2024 - Biased Outputs. Generative AI results can reflect inherent biases in the underlying data. In response, generative AI companies need to assign experts to review the data and results and correct for bias through oversampling and other statistical techniques.

September 30, 2025 - Application of large language models in healthcare continues to expand, specifically for medical free-text classification tasks. While foundation models like those from ChatGPT show potential, alternative models demonstrate superior accuracy and lower costs. This study underscores significant ...

But ChatRWD, a retrieval-augmented generation (RAG) system – which essentially combines LLMs with retrieval systems to improve output – produced useful answers to 58% of the questions (compared with 2%-10% for the LLMs).

September 29, 2025 - According to research, generative AI shows big promise in healthcare; 40% of all healthcare working hours are consumed by natural language tasks—documentation, charting, and reporting—that large language models (LLMs) can transform.