Generative AI in Healthcare
Artificial intelligence systems have spurred a new wave of innovation in healthcare. From diagnosing diseases with exceptional precision to accurately predicting a patient’s risk level for developing certain conditions, AI-enabled healthcare technologies provide physicians, researchers, and other healthcare professionals with invaluable capabilities.
Advances in generative AI, a type of artificial intelligence capable of creating original content, have further increased the value of applying AI systems to medical use cases. In this article, we’ll explore what generative AI is and share use cases that demonstrate the power of generative AI in healthcare.
What Is Generative AI?
Generative AI is a subset of artificial intelligence designed to produce original content, including images, text, music, and synthetic data. This branch of AI differs significantly from traditional AI models because generative AI systems don’t depend on preexisting data for analysis, predictions, and decision-making. Instead, they learn from patterns and structures in the data to generate new content that’s often impossible to distinguish from content created by humans. Using advanced machine learning techniques such as deep learning and neural networks, generative AI models can understand and replicate the data's underlying characteristics.
What Makes Generative AI So Valuable for Healthcare Use Cases?
Generative AI has several unique attributes with the potential for accelerating innovation and advancement in healthcare. Here are three of its key capabilities.
Original content generation
With its ability to create new content, generative AI has exciting implications for clinical research. Generative AI systems can produce synthetic patient data after being trained on the characteristics of real data. The use of artificial patient data allows medical researchers to more easily protect patient privacy. Synthetic data is also useful for expanding or balancing data sets in certain research contexts.
Data analysis and predictive analytics
Remote patient monitoring devices are used to regularly monitor patient risk factors such as blood pressure, weight, and blood glucose levels. Generative AI algorithms can analyze data from these devices, detect anomalies in real time, and generate personalized care recommendations.
Natural language processing (NLP) capabilities
Natural language processing is a generative AI model enabling machines to understand the meaning of spoken and written language. This technology can be used to analyze and generate clinical documentation, automate medical transcription, and extract relevant information from medical records, improving accuracy and freeing healthcare professionals to focus on other tasks.
The Power of Generative AI in Healthcare
Generative artificial intelligence is changing how providers and healthcare systems approach diagnosis, treatment, and patient care. Here are seven exciting use cases that illustrate the impact of generative AI in healthcare.
AI-assisted clinical decision-making
AI systems can provide decision support to physicians, radiologists, and other healthcare professionals. By training on images of healthy human tissue, generative AI models can spot subtle abnormalities that even highly trained clinicians may miss. By flagging potential anomalies in medical images, generative AI can assist physicians in proactively identifying cancerous tumors and other aberrations.
Medical imaging and diagnostics
Generative AI algorithms can detect subtle changes in longitudinal medical images such as X-rays, CT scans, and MRIs. These incremental changes can be analyzed to predict how a patient’s condition is likely to progress, providing a clearer picture of the probable disease trajectory and helping physicians make more informed treatment recommendations.
Generative AI is accelerating the adoption of personalized medicine. It can analyze vast quantities of patient data, including genetic information, medical history, and the DNA of specific types of cancers. Using this individualized data, generative AI algorithms can produce patient-specific treatment plans, such as cancer treatments targeting particular genetic mutations fueling a cancer’s growth.
Drug discovery and development
Generative AI's ability to create hyper-realistic simulations of real-world processes is accelerating the drug discovery process. One application of this technology is molecular simulation, a time-consuming process that involves physically testing candidate drug compounds. Using generative AI algorithms, clinicians can run high-fidelity molecular simulations in a virtual environment, reducing the need for resource-intensive traditional chemistry methods.
Virtual assistants and chatbots
AI-powered virtual assistants and chatbots can interact with patients conversationally, helping them access services when it is convenient for them. Integrated with major electronic health record (EHR) systems, virtual assistants can assist patients to book, reschedule, get wait-listed, and cancel appointments independent of human appointment schedulers. In addition, this technology can also be used to assist physicians with routine tasks such as checking a patient’s medical history or ordering tests.
Advanced simulations for training
Simulations allow medical professionals and researchers to improve their skills and knowledge in controlled environments. Generative AI creates realistic models for medical simulations and training purposes. With hyper-realistic training environments, healthcare professionals can engage in complex medical procedures and improve their skills and decision-making abilities without real-world consequences.
Clinical trial matching
Natural language processing algorithms can sift through enormous amounts of unstructured clinical and patient data. Using NLP, medical researchers can accurately identify patients eligible to participate in clinical trials. Quickly matching eligible patients with clinical trials accelerates the pace of medical research and also gives patients access to potentially beneficial experimental treatments.
Unlock the Potential of Generative AI with Snowflake
Snowflake unlocks the full potential of generative AI in healthcare. With its robust architecture and scalability, Snowflake provides the necessary infrastructure for handling and processing large-scale data sets, the foundational components for training and deploying generative AI models.
Snowflake’s recent acquisitions of Streamlit, Neeva, and Applica bring the latest AI technologies to the Data Cloud. With Streamlit, data scientists quickly build data apps independent of front-end developers. Neeva facilitates generative AI-enabled searches, empowering teams to discover precisely the right data point, data asset, or data insight. Applica enables teams to search unstructured data, allowing Snowflake’s customers to easily leverage unstructured data in the Data Cloud.
Additionally, Snowflake includes built-in advanced security features and data governance controls, so healthcare organizations have the tools required to build and maintain cutting-edge generative AI systems while protecting their patients’ sensitive healthcare information from compromise. In addition, Snowflake Marketplace allows data engineers and data scientists to acquire the third-party data needed to train machine learning models for AI.