How Generative AI is Transforming Healthcare
In the future, generative AI could support real-time patient monitoring, along with data analysis to generate personalized insights that encourage healthy behaviors or lead to timely interventions before medical conditions worsen. Generative AI could also make imaging solutions more accurate and transferable across different practice areas. Finally, the technology’s adaptability and interactivity could encourage preventive care, wellness, and healthy behaviors via personalized nudges on mobile apps, wearables, and monitoring devices. On the administrative front, Doximity, Abridge, and DeepScribe are exploring applications that automate processes such as documentation, claims handling, preauthorization and appeals, patient onboarding, and scheduling. Paige.AI, a digital pathology company, is integrating generative AI into its products to improve the accuracy and efficiency of prostate cancer detection. It was the first company to receive FDA approval for AI use in digital pathology and is looking to integrate the resulting information into patient electronic health records along with other clinical data.
When it comes to large language models, Google has been playing catchup to OpenAI, the startup behind the viral chatbot ChatGPT, which has received $10 billion investment from Microsoft. In 2022, Microsoft acquired Nuance Communications for $18.8 billion, giving it a major foothold to sell new AI products to hospital clients, since Nuance’s medical dictation software is already used by 550,000 doctors. “Nuance has an enormous footprint in healthcare,” says Alex Lennox-Miller, an analyst for CB Insights, which makes Microsoft “well-positioned” for the use of its generative AI software for administrative tasks in the sector. In 2021, Google disbanded its standalone Google Health division but said health-related efforts would continue across the company.
Applications of Generative AI
It’s inspiring an explosion of ideas around use cases, from reviewing medical records for accuracy to making diagnoses and treatment recommendations. In April, Med-PaLM 2, our medically-tuned version of PaLM 2, was made available to a select group of customers to explore use cases and share feedback. Through our close work with these early testers, we’ve been able to progress the technology and are ready to share with more customers. Next month, we’ll make Med-PaLM 2, which supports HIPAA compliance, available as a preview to more customers in the healthcare and life sciences industry — a critical step to developing our LLMs safely and responsibly. The blog shares how Bayer Pharmaceuticals is trialling generative AI solutions such as Med-PaLM 2 and Google Cloud’s Vertex AI to see how they can assist in bringing drugs to market. Because healthcare is so highly regulated and the consequences of mistakes are high, generative AI use cases need to start out very small.
Gen AI can help private payers’ operations perform more efficiently while also providing better service to patients and customers. Protect your most valuable data in the cloud with Oracle’s security-first approach and comprehensive compliance programs. Oracle provides visibility and machine-learning–driven insights to ease management across all layers of the stack deployed on any technology, anywhere. Our data science team is excited about bringing the latest in machine learning to our customers to help them with real life business problems. In a VAE, a single machine learning model is trained to encode data into a low-dimensional representation that captures the data’s important features, structure and relationships in a smaller number of dimensions.
What is Generative AI and how can it benefit healthcare organizations?
It’s moving the needle when it comes to patient engagement and knowledge transfer among clinicians. Plus, it’s bringing a new level of intelligence and efficiency to everyday tasks like data collection and processing. AI sophistication and efficacy are advancing exponentially along with its adoption throughout the healthcare industry.
- The AI ingests patient data from the past 12 hours, including lab results, medication, important events, and spits out a transfer summary, that also includes suggestions for what the oncoming nurse should be thinking about in the next 12 hours, says Schlosser.
- For example, email and text messages can remind patients to make appointments for annual check ups, follow up visits, prescription renewals, flu shots, and routine procedures like mammograms.
- This work builds on our ongoing collaboration with Bayer to accelerate drug discovery with high-performance compute power, which includes efforts to run Bayer’s large quantum chemistry calculations at scale with Google Cloud Tensor Processing Units (TPUs).
- For instance, patients’ consent can’t be easily exercised in the case of an unlearning process.
Such uses range from continuity of care to network and market insights to value-based care (see sidebar, “Potential uses of generative AI in healthcare”). Going forward, generative AI-powered tools could be used to monitor public health and allocate resources. In the US, Medicaid could potentially leverage the technology to better manage allocations based on genrative ai health data and forecasted need. The FDA could use it when reviewing the safety and efficacy of drugs, and generative AI could help public-health groups like Doctors Without Borders predict outbreaks and mobilize resources to minimize impact. Beyond drug discovery, generative AI could accelerate and improve clinical trials and precision medicine therapies.
It learns from the available data to estimate the response of a target group to advertisements and marketing campaigns. Generative AI better identifies an ailment to help patients receive impactful treatment even during the early stages. In addition, it can also help companies opt for impartial recruitment practices and research to present unbiased results. While the most popular art NFTs are cartoons and memes, a new kind of NFT trend is emerging that leverages the power of AI and human imagination. Coined as AI-Generative Art, these non-fungible tokens use GANs to produce machine-based art images. Some people are concerned about the ethics of using generative AI technologies, especially those technologies that simulate human creativity.
And while you have the health consumer’s attention, you can prompt them to schedule a flu shot or update their information. For instance, patients’ consent can’t be easily exercised in the case of an unlearning process. Removing selected data points from a model might affect the performance of the model itself.
Generative AI for healthcare diagnostics and drug discovery
Others are working to improve resource utilization by both clinical and administrative staff. We continue to believe that in-person interaction helps us build trusting relationships and a welcoming, supportive culture that fosters innovation. That’s why our new working model includes essential time for face-to-face collaboration among teams each quarter—such as for strategic planning, project kick-offs, brainstorms, and retrospectives. It also integrates a smooth flow of real-time communication by having each team’s members operate within three time zones of one another, to facilitate at least six hours of overlap each day. Our integrated suite of applications with built-in AI capabilities connects your most critical business processes and provides consistent user experiences—so you can get more done. There are AI techniques whose goal is to detect fake images and videos that are generated by AI.
With generative AI, learning algorithms can review the raw data programmatically and create a narrative that appears to have been written by a human. Executives embarking on digital and CX transformation initiatives to address healthcare consumerism are undertaking the most significant technology and change management projects the industry has seen in more than a decade. Many Generative AI platforms frame their value proposition around the speed with which content teams can create copy. In January 2023, Google announced that it had developed a specialized version of its PaLM large language model called MedPaLM, trained on medical information to answer medical questions.