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The Future of Patient Safety: Exploring Generative AI in Healthcare

generative AI in healthcare

Generative artificial intelligence (AI) is spearheading a new era of innovation, fundamentally reshaping how medical services are delivered and research is conducted. Generative AI in healthcare, through its ability to create new data and simulations, offers unprecedented opportunities for medical breakthroughs and improving patient safety. By leveraging generative AI in healthcare, organizations can predict, prevent, and manage health risks with unprecedented precision and efficiency.

What is Generative AI?

Generative AI represents the next wave in artificial intelligence, capable of producing new, original content from initial inputs, unlike traditional AI, which is designed to perform specific tasks within a set framework based on predefined rules and data analysis. Traditional AI excels in analyzing data and making predictions within its programmed scope, evident in applications like voice assistants and recommendation systems.

In contrast, generative AI, through models like GPT-4, can create entirely new content, from text to images, expanding AI’s role from mere data analysis to actual content creation, thereby broadening the horizons for innovation and creativity in various fields.

Generative AI in Healthcare: A Paradigm Shift

AI in healthcare refers to a class of artificial intelligence that can generate new content, including images, text, and even synthetic data. This is achieved through advanced algorithms and neural networks that learn from existing data sets to produce new, original outputs. This capability opens doors to numerous applications in healthcare, from drug discovery to personalized medicine.

This power is crucial in healthcare, where data-driven decisions can significantly impact patient outcomes, enhance patient safety, and improve staff well-being.

According to McKinsey, generative AI in healthcare is a meaningful new tool that can help unlock a piece of the unrealized $1 trillion of improvement potential present in the industry.

Use of AI in Healthcare

According to KLAS Research, generative AI in healthcare is currently being adopted mostly by larger organizations. Several organizations, including payers, have rapidly adopted a range of generative AI tools offered by EHR vendors like Epic, as well as tech giants such as Google, Nuance, and OpenAI. This greater uptake by larger entities is related to their abundant resources and easier access to large amounts of data, facilitating quicker adoption of generative AI technologies.

Furthermore, while just a quarter of those surveyed by KLAS have already put generative AI solutions into practice, a notable 58 percent indicate that their organization is likely to implement or acquire such a solution in the coming year.

A survey by the Institute for Healthcare Improvement revealed that those who are using AI for healthcare quality improvement are using it to brainstorm changes (25 percent), analyze problems (16 percent), and build solutions such as standard work (16 percent).

Generative AI applications are now emerging across all healthcare sectors, encompassing providers, pharmaceutical companies, insurers, medical technology firms, service providers, and public health organizations. Some potential uses of AI in healthcare sectors are outlined below:


  • Synthesize and recommend tailored risk considerations for patients based on their medical history and existing medical literature
  • Generate personalized insights that encourage healthy behaviors or lead to timely interventions before medical conditions worsen
  • Generate a list of current conditions and potential codes based on voice, electronic medical records (EMRs), text, and other data


  • Analysis of patient data to identify subgroups likely to respond to specific treatments or to personalize drugs to the unique needs of individual patients
  • Digital modeling of clinical trials


  • Custom coverage summaries for specific benefits questions (online and via call-center contacts)
  • Generate summaries of manual and denied claims issues and sources to determine solutions

Public-Health Agencies

  • Improve resource planning and allocation
  • Anticipate public-health needs and interventions

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Examples of Artificial Intelligence in Healthcare

Generative AI is being increasingly utilized in healthcare to enhance patient experiences and improve staff productivity. For instance, it is used to improve precision in patient positioning and CT image reconstruction.

Another example is the use of generative AI technology, such as GPT-4, to model how a healthcare clinician might use new platforms to convert patient interactions into clinician notes quickly. Companies like Microsoft are designing clinical tools that use generative AI to enhance productivity and ease the burden on healthcare providers.

Some other examples of generative AI in healthcare include:

  • IBM Watson Health: This AI platform assists in cancer diagnosis and treatment, offering insights drawn from vast medical literature.
  • Google DeepMind Health: Focuses on AI research to improve the diagnosis of eye diseases and breast cancer.
  • Serena: Built on cutting-edge Natural language processing (NLP), Serena is a virtual mental health companion trained on real therapy sessions.
  • Profluent: Created a machine-learning model able to generate new protein sequences with specific functions, an innovative tool that can help improve access to affordable treatments.

    Generative AI in Healthcare Infographic (1)

The Impact of Generative AI on Patient & Employee Safety

Generative AI is transforming healthcare by significantly improving patient and employee safety. Through advanced applications in predictive analytics, medical imaging, and robotic surgery, this technology is not only enhancing the accuracy of diagnoses and treatments for patients but also providing critical support in ensuring the well-being and efficiency of healthcare workers.

Improving Patient Safety

Predictive Analytics: AI excels in predicting patient health risks by analyzing vast datasets. This includes identifying potential adverse drug reactions, foreseeing complications in high-risk patients, and suggesting personalized treatment plans.

Predictive analytics can also be used to predict future harmful flu or COVID-19 strains. BioNTech recently acquired InstaDeep, an AI decision-making system, in order to develop an early-warning system for new COVID-19 variants. This allows healthcare organizations, health authorities, and vaccine developers to plan accordingly for future health emergencies.

Medical Imaging: AI algorithms can now interpret medical images, such as X-rays or MRIs, with remarkable accuracy. This not only speeds up the diagnostic process but also reduces the likelihood of human error, enhancing patient safety.

Robotic Surgery: Generative AI in healthcare aids in robotic surgery by providing precise, data-driven guidance. This reduces surgical errors and improves patient outcomes, particularly in complex procedures.

Optimizing Employee Safety for Healthcare Workers

Ergonomic Assessments: AI systems can analyze the physical movements of healthcare workers, identifying patterns that could lead to injury and suggesting ergonomic improvements.

Workload Optimization: By predicting patient influx and health trends, AI can help in staffing and resource allocation, reducing burnout and physical strain on healthcare professionals.

Training and Simulation: Generative AI can create realistic training simulations, allowing healthcare workers to practice challenging scenarios in a safe environment, thereby improving their readiness for real-life situations.

Ethical Considerations & Challenges

While the benefits of generative AI in healthcare are immense, addressing the ethical concerns is crucial. Issues like data privacy, consent, and the potential for AI bias must be carefully managed to ensure that these innovations benefit all patients equitably.

According to KLAS research, respondents report the top three challenges to AI in healthcare are accuracy/reliability, cost/ROI, and security/privacy. These challenges can be surmounted by clearly outlining the recommended applications of these tools for process assistance, conducting data-driven audits, and implementing robust security protocols.

The European Union has set a precedent with its AI Act, establishing detailed obligations and penalties for AI system developers, aiming to balance safe technological development with the encouragement of innovation. This act, focusing on risk-based classification of AI products and stringent controls for high-risk categories, contrasts with the U.S.’s context-specific approach, lacking clear penalties for noncompliance.

The EU’s framework, which includes mandatory rights impact assessments for high-risk AI systems and specific prohibitions on certain AI applications, may serve as a model for future U.S. regulations, highlighting a move towards more structured governance in the rapidly evolving AI landscape.

Generative AI in healthcare is not just a futuristic concept but a present-day reality with enormous potential to improve patient and employee safety. By harnessing the power of AI, healthcare providers can offer more precise, efficient, and personalized care, making the health system safer and more resilient for everyone.

This article was written and contributed by Cindy Chamness, MBA, RN, CPHQ, CPPS, a seasoned healthcare operations executive with over three decades of experience. Cindy currently works as a Patient Safety and Healthcare Tech Advisor, leveraging her extensive knowledge in quality performance, patient safety, and risk management.

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