The emergence of Generative AI (GenAI) is reshaping healthcare use cases and facilitating the rapid personalization of patient care. According to a December 2023 survey conducted by Gartner Healthcare Provider Research Panel, 84% of healthcare provider executives believe large language models (LLMs) will have a significant (35%), transformative (37%), or disruptive (12%) impact on the healthcare industry.
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For example, tasks such as analyzing and protecting patient data, interpreting medical images, devising personalized treatment plans, etc., have become more streamlined with the increasing adoption of GenAI. There are numerous applications for GenAI in healthcare, and we’ll talk about a few trending ones in this article.
How is Generative AI Helping Healthcare Operations
Medical note summarizing: GenAI models can generate discussion-based summaries of patient visits, encompassing key details such as primary concerns, medical history, examination findings, test results, and treatment plans. For example, Amazon AWS Healthscribe offers software vendors a platform to develop clinical applications that automatically generate clinical notes by analyzing conversations between patients and healthcare professionals.
Remote patient monitoring: Healthcare providers (HCPs) can receive real-time alerts on patient health, enhancing communication and coordination processes.
Medical information and education: GenAI facilitates the creation of personalized educational materials, providing immersive training experiences that help medical professionals acquire new skills and techniques.
Predictive maintenance: GenAI can help predict when medical devices require maintenance, enabling HCPs to receive early alerts of potential risks or failures, thus preventing device malfunctions.
Medical device submissions: GenAI helps generate documentation such as the Premarket Notification 510(k) and Premarket Approval (PMA) applications, streamlining compliance workflows.
Diving into GenAI-powered Applications
GenAI has a wide range of applications within the healthcare industry, spanning areas such as Medical Imaging, Personalized Medicine, Drug Discovery and Development, Medical Research, Data Analysis, Risk Predictions, and Administrative Functions.
One of the examples is using generative AI to build personalized content for HCP interactions. With Gen AI you can ensure that the collaterals used by commercial pharma teams are updated, tailored to specialized HCP, and impactful.
Below, we share illustrative applications across these major generative AI use cases in healthcare and pharma.
Medical Imaging
Image synthesis: GenAI models can synthesize organ or tissue images, aiding in educational endeavors for training medical professionals and simplifying medical explanations to patients via visually relatable models.
Automated segmentation: GenAI streamlines the segmentation of organs or specific conditions in medical images, effectively saving time and expediting image analysis.
Pathology prediction: GenAI helps analyze patterns within medical images, thereby identifying or predicting medical conditions, facilitating early detection, and personalized healthcare strategies.
Personalized Medicine
Tailored treatment plans: GenAI helps analyze patient data, including genetic information, unique medical history, and clinical data, to offer advice for personalized treatment plans, empowering HCPs to offer tailored treatment options.
Predictive analytics: GenAI analyzes vast datasets, offering insights to practitioners regarding estimates of disease progression and likely treatment outcomes, thus providing valuable clinical decision support.
Real-time clinical decision support: GenAI provides real-time, evidence-based recommendations by analyzing complex datasets concerning a patient’s genetic profile, expediting decision-making with timely insights.
Drug Discovery & Development
Compound generation: Researchers leverage GenAI models to accelerate drug discovery by efficiently exploring a diverse chemical space.
Predicting drug-drug interactions: GenAI helps practitioners forecast potential drug interactions, thereby assisting researchers in evaluating the safety and efficacy of medication combinations.
Clinical trial design: GenAI utilizes historical clinical trial data to refine trial designs, identify suitable patient cohorts, and assess potential challenges, among other aspects.
Medical Research & Data Analysis
Data processing: GenAI analyzes extensive medical data, automating data extraction and streamlining administrative processes, allowing researchers to focus more on critical work aspects.
Medical document summarization: GenAI summarizes lengthy medical documents, providing concise subject overviews for researchers.
Predictive analytics insights: GenAI leverages vast amounts of historical medical data to provide insights into possible outcomes, helping researchers make informed decisions.
Risk Predictions
Early detection & surveillance: GenAI processes real-time data from diverse sources, such as social media and daily health reports, facilitating the identification of early signals of emerging public health concerns.
Enhancing supply chain resilience: GenAI boosts healthcare supply chain management by predicting and mitigating possible disruptions, analyzing global logistics, tracking inventory levels, and forecasting demand to ensure seamless continuity.
Scenario planning & preparedness: GenAI simulates different scenarios using historical data and current conditions, helping organizations prepare for potential outcomes such as sudden spikes in hospitalizations due to the spread of infections.
Administrative Functions
Automated documentation & record-keeping: GenAI automates documentation tasks, such as transcribing medical notes or updating patient records.
Data entry & extraction automation: GenAI automates tasks and extracts pertinent information from various sources to minimize manual data entry, boosting speed and accuracy.
Automated regulatory compliance:GenAI automates regulatory compliance checks, supporting human experts to accelerate checking speed and gather insights.
The volume of healthcare data is growing dramatically, placing significant pressure on HCPs to collect, sort, analyze, and structure more and more data. GenAI’s ability to work with data and structure it for insights and automation is providing the greatest breakthroughs in various applications.
A Clear Roadmap for GenAI Integration
GenAI holds immense promise in healthcare, yet its integration demands careful consideration and thoughtful planning due to the sensitive nature of patient healthcare information, making data security paramount. It is crucial to acknowledge that GenAI’s efficacy depends on the quality of the data it is trained with, and biases within datasets can lead to erroneous responses, potentially resulting in serious repercussions within healthcare settings. Thus, the presence of expert “human in the loop” systems is imperative to ensure that GenAI suggestions positively impact patient outcomes.
For example, under expert human oversight, GenAI can generate discharge summaries in a patient’s native language or create lab summaries from doctors’ rounds. Moreover, its ability to synthesize human language can refine Electronic Health Records (EHRs), reducing manual inputs and mitigating human errors. GenAI can potentially improve communication and transparency within healthcare ecosystems and streamline decision-making processes. For instance, it can simplify explanations of benefits (EOBs) and health plan coverage for users.
However, the possibility of failure looms without effective governance and integration strategies. A comprehensive governance roadmap should consider the following elements:
A clear assessment of organizational goals regarding AI usage
Defined objectives regarding metrics such as patient outcomes and satisfaction scores
An infrastructure audit to assess the efficacy or possibility of AI integration
A skills audit to determine whether existing human resources possess the requisite capabilities
Establishment of robust data governance structures to ensure security and privacy
Consensus on performance metrics and a strategy for scaling AI applications in tandem with infrastructure and skills development will facilitate the gradual expansion of GenAI in critical healthcare environments.
Sudhakaran Jampala
Sudhakaran Jampala is a Content Writer (Marketing) with Straive, specializing in the cutting-edge technology areas of data science, machine learning, and AI. He is fascinated by the art of storytelling, which transforms data into sparkling insights by revealing patterns and infusing visual narratives. Sudhakaran is an ardent believer in the transformative potential of AI and its multiple flavors (e.g., Generative AI) in reshaping enterprises and communities constructively. Thus, he devotes a considerable portion of his leisure hours to understanding the intersections of AI with the key challenges of the 21st century. A father of two children, Sudhakaran enjoys watching them grow and flourish.
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