Generative AI in Healthcare

Excitement for generative artificial intelligence (genAI)—a branch of artificial intelligence using algorithms to create new videos, images, and text that resembles its reference data—is quickly spreading. This technology is different from other types of AI by using predictive models to make predictions based on existing patient data. Unlike traditional AI, which focuses on detecting patterns, making decisions, gathering analytics, and classifying data, genAI produces new content, chat responses, designs, and synthetic data.

Already, it is leveraged within a handful of industries, from engineering to marketing, and even law. And now, tech mammoths are set on integrating generative artificial intelligence into healthcare. From streamlining medical processes like scribing prescriptions to responding to patient messages, the list of potential AI use cases is endless. But that hasn’t stopped experts from scrutinizing where genAI’s shortcomings could set up the sector for failure.

Noticing an uptick in documentation around mentions of genAI within the life sciences segment, we dove deeper into the research to outline the recent developments of genAI. From its proposed applications to the AI technology that companies have already released to where experts are saying it will fall short, we’ve compiled the most crucial insights to keep you up on how this groundbreaking technology is disrupting the healthcare space.

GenAI in Healthcare

According to a new report from Accenture, advances in large language AI models, which genAI uses, can revolutionize the healthcare industry, benefiting creativity and boosting productivity for providers and patients. Nearly all (98%) providers and (89%) executives who participated in the study believe these advancements will usher in enterprise intelligence, as 40% of all working hours could be supported or augmented by language-based AI.

The reality is that a large percentage of medical institutions face scarce clinical resources. The biggest potential for generative AI in healthcare, then, is to free overworked and underpaid employees from administrative tasks so they can work at the top of their license. “A strong digital core and investments in people will be the key to reaping the full value of generative AI in a responsible and authentic way,” Accenture writes.

However, to truly realize the full benefits of genAI, upper management will have to remodel the work, and consequently, jobs done by human beings. Ultimately, generative AI will take over some tasks—but not whole jobs. Those jobs, in turn, will have to be repurposed so that their new “9 to 5” focuses on tasks that cannot be automated and, therefore, prioritizes human efficiency and effectiveness. 

But the work of integrating genAI into healthcare starts with helping both clinicians and patients stay up-to-date with this technology for greater access, better experiences, and improved outcomes. Further, institutions will need to get their proprietary data ready. Foundation models for genAI need vast amounts of curated data to learn, which means organizations need to take a strategic and disciplined approach to acquiring, vetting, safeguarding, and deploying data.

Fine-tuning the pre-trained large language models with organization-specific data will allow for more accurate usage. Organizations need a modern enterprise data platform built on the cloud with a trusted set of data products. As more than half of healthcare organizations plan to use ChatGPT for learning purposes, and more than half are planning pilot cases this year, these steps will be critical in ensuring a positive outcome.

Use Cases of GenAI in Healthcare

Clinical Decision-Making 

Generative AI excels at analyzing complex and diverse information—making it a suitable medical option for pinpointing potential health problem areas and recommending appropriate further steps. It acts as a virtual collaborator, helping healthcare providers consider a broader spectrum of variables, thus contributing to more comprehensive and personalized patient care.

Moreover, genAI doesn’t just assist with diagnosis; it also plays a crucial role in suggesting tailored treatment options. By drawing on vast databases of medical knowledge and the latest research, this technology can propose evidence-based therapies and interventions, ensuring that patients receive the most effective and individualized care.

As genAI continues to evolve, its potential to revolutionize clinical decision-making is undeniable. Primary care providers are now empowered with a valuable ally, capable of sifting through vast amounts of data, recognizing patterns, and offering valuable insights that can ultimately lead to improved patient outcomes.

Risk Prediction for Catastrophic Health Events

Generative AI models are becoming indispensable in predicting catastrophic health events. Not only do they offer valuable insights for scientists studying pandemics and preventive measures, but they can also analyze vast datasets, help identify new antibodies to combat infectious diseases, and understand their etiology.

Additionally, genAI aids in constructing data-driven outbreak responses, leveraging transmission dynamics and epidemiological trends. These advancements are revolutionizing our approach to health crises, equipping us with powerful AI tools for risk prediction and mitigation and ultimately enhancing global healthcare preparedness.

GenAI models have emerged as a vital resource for modeling new pandemics and developing preventive measures. Some new generative AI models are currently being trained on large amounts of protein sequences to identify new antibodies that could address infectious diseases and construct outbreak responses for the future.

Personalized Medication and Care

In the rapidly evolving landscape of healthcare, personalized medication and care have taken center stage, thanks to the integration of wearable technology and generative AI.

 Wearable devices, equipped with sensors and sophisticated technology, enable the real-time and continuous collection of essential health indicators. These include heart rate variability, blood oxygen saturation levels, blood glucose levels, and more. This wealth of data not only empowers individuals to gain insights into their own health, but also offers a transformative opportunity for healthcare providers to transition from traditional reactive healthcare models to more proactive, patient-centric ones.

Wearable technology’s continuous data collection capabilities create a dynamic and comprehensive view of an individual’s health status. Generative AI plays a crucial role in processing and making sense of this vast amount of data. It excels at identifying trends, anomalies, and potential health risks, even before they manifest as symptoms. This predictive capability is a game-changer for healthcare, as it enables early intervention and personalized treatment plans, tailored to an individual’s unique health profile.

Furthermore, the synergy between wearable technology and generative AI extends beyond monitoring. It fosters a collaborative approach between patients and healthcare providers, promoting active engagement in one’s health. Patients can share their data securely with their healthcare teams, enabling a more holistic understanding of their health and fostering a proactive approach to wellness.

Improved Drug Discovery and Development

Generative AI has shown promising results in drug discovery and development (e.g., Insilico Medicine, a company that has developed a generative AI platform called GENTRL). Doctors may no longer have to rely on old-fashioned methods such as manual patient diary entries, faxed medical records, and snail-mailed findings to regulatory agencies.

The advent of generative AI is poised to revolutionize these practices. It allows for the efficient analysis of vast datasets, swiftly identifying promising candidates for clinical trials, optimizing molecular structures, and even predicting potential side effects and interactions. This unprecedented level of data processing not only expedites the discovery process but also enhances the precision and safety of drug development.

How GenAI is Transforming the Healthcare Industry

Generative AI is ushering in a profound transformation within the healthcare industry, reshaping the way care is delivered and managed on a macro scale. By harnessing the power of artificial intelligence, genAI has unlocked capabilities that were previously inconceivable, fundamentally altering the landscape.

In the past, treatments were often administered based on broad population data, with limited consideration for individual variations. GenAI, however, has made it possible to delve deep into patients’ genetic profiles, medical histories, and real-time health data. This means that healthcare can now be tailored to the unique needs and genetic makeup of each patient. The result: a more precise, effective, and patient-centric approach to medical care that improves outcomes and reduces the occurrence of adverse effects significantly.

Furthermore, genAI is streamlining drug discovery and development processes. Historically, the path from drug discovery to market availability has been arduous and time-consuming. By accelerating the identification of potential drug candidates, optimizing molecular structures, and even predicting side effects and drug interactions, the speed and efficiency enabled by genAI holds the promise of bringing novel and safer medications to patients. 

Moreover, the healthcare industry is becoming increasingly data-driven thanks to genAI. The technology’s capacity to analyze vast datasets, detect trends, and make predictions is invaluable for proactive disease management, efficient resource allocation, and evidence-based decision-making. 

These data-driven insights are reshaping public health strategies, optimizing hospital operations, and enhancing care delivery at large, ultimately leading to improved patient care and increased sustainability of healthcare systems. In this macro lens view, genAI is paving the way for a more responsive, patient-focused, and data-enhanced healthcare ecosystem that was simply beyond reach in the pre-AI era.

Industry Advancements and Growing Competition

While genAI is relatively new to the market, there’s no shortage of startups and established companies racing to be the next manufacturing leader of genAI-powered healthcare tech.

This past April, Microsoft unveiled plans to embed generative AI into clinical software from Epic, the biggest hospital EHR vendor in the U.S. The two companies produced the first sites integrating GPT into EHR workflows to automatically draft replies to patient messages. The companies are also bringing genAI to Epic’s hospital database, allowing laypeople to ask AI general questions instead of needing a data scientist to query specific data.

Google is also joining the race by creating its large language model, called Med-PaLM 2, which is available to specific customers for use case trials. Unlike most AI algorithms, Med-PaLM was specifically trained on medical data, allowing it to sift through and make sense of massive amounts of healthcare information. While it still has room for improvement in answering queries, its performance is exceeding expectations. 

Unlike Microsoft’s product, Google’s Med-PaLM won’t be for patient-facing use. Rather, hospitals could use Med-PaLM generative AI technology to analyze data to help diagnose complex diseases, fill out records, or as a concierge for patient portals.

Another use case for genAI is streamlining medical note-taking. Physicians can spend roughly six hours a day logging notes in their EHR, which leads to less time with patients and more burnout. Nuance, a documentation company owned by Microsoft, is providing a solution to this problem. 

Last month, the company announced the integration of GPT-4 into its clinical note-taking software. This summer, Nuance is allowing providers to beta test the documentation products, called DAX Express. Between 300 to 500 physicians will test the product in a private preview this mid-June, before it’s generally available in early fall.

Suki, a documentation company partnering with Google, recently launched its genAI-powered “Gen 2,” which can generate a clinical note by listening into a conversation and filling in a note automatically. By the end of the year, doctors will also be able to ask the AI questions and give it commands, such as graphing a patient’s A1C levels over a three-month period.

Backlash Against Generative AI

There’s no shortage of excitement around future use cases for genAI. And yet, some of its current applications have already drawn sharp backlash.​​ 

ChatGPT and GPT-4, bi-products of largely consumer-focused genAI that utilize interfaces from OpenAI, are capable of holding conversations, answering questions, and even writing low-grade high school essays. However, this past February when the New York Times published their troubling conversations with Bing’s chatbot, Sydney, which sent messages that ranged from promoting infidelity to expressing a desire to be “alive,” speculation arose concerning where these tools are pulling their reference data from.

While tech leaders are hyping up genAI’s streamlining capabilities, conversations around automation and the role it will play in our society are running rampant, especially with regard to the healthcare sector. Ultimately, it’s a question of how much we should be relying on genAI and what the consequences of doing so are. 

An AI system relies on a machine learning model to generate answers or make decisions. But how these results are produced brings into question three areas of ethical concern for society: privacy and surveillance, bias and discrimination, and the role of human judgment. 

Because large language models can provide responses to queries that are factually incorrect, irrelevant, or frankly disturbing, GPT-4 “still has many known limitations” that OpenAI is working to address, such as social biases, hallucinations, and adversarial prompts.

Bias then becomes another key worry with AI, especially within a healthcare setting. If an algorithm is trained on biased data, or its results are applied in biased ways, it perpetuates those biases. This leads to the question of accountability: since generative AI, and AI applications in general, are relatively new, it’s unclear if the owner, user, or developer is at fault for any negative consequences stemming from its use.

The Future of Generative AI in Healthcare

Generative AI in healthcare holds immense promise and is poised to revolutionize the industry. But while the future of this technology holds enormous potential within healthcare and outside of it, it also raises ethical and regulatory challenges, such as patient privacy and security, standardization, and ensuring equitable access to AI-powered healthcare. As these issues are addressed, genAI will usher in a more patient-centered, efficient, and data-driven field. Ultimately, a new era of improved health outcomes and more accessible medical services is just years, if not months, from being a reality.

Getting the Answers You Need

In a volatile market that’s producing new developments around genAI every day, it’s challenging to decipher what technology, product, or insight could revolutionize the healthcare industry next. Cutting through the noise to find insights is nearly impossible in the age of information overload. You need a tool that does all of the heavy lifting, so you can focus on leveraging information rather than searching for it. 

AlphaSense is a leading provider of market intelligence, including 10,000+ high-quality content sources from more than 1,500 leading research providers—all in a single platform. Analysts, researchers, and decision-makers in the medtech sector can access exclusive research reports only found elsewhere in disparate locations and often behind expensive paywalls. With AlphaSense, companies can conduct comprehensive research that gives them a competitive edge with smarter, more confident decision-making.

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Don’t miss our State of Generative AI & Market Intelligence Report 2023.

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