Exploring the Use of Generative AI in Healthcare and Medicine With GPT taking the world by storm, the age of Generative AI has truly begun In the realm of healthcare & medicine, this cutting-edge technology holds immense potential to transform patient care, diagnostics, and treatment planning.
The search giant announced partnerships this year with health systems like Mayo Clinic to use generative AI, or algorithms that can be used to create new content like text, to improve clinician workflows. Besides, incorporating real-time updates and vast medical databases, generative AI offers insights from the latest research and global trends. As a result, healthcare professionals benefit from a dynamic and evolving knowledge base, optimizing patient care pathways. Generative AI in healthcare provides a plethora of advantages to healthcare providers, patients, medical institutions, and other relevant stakeholders. These benefits include enriched decision-making, heightened patient engagement, increased access to healthcare, and streamlined health data management. Leveraging vast datasets like EHRs, AI, and ML enables healthcare providers to mine past treatment and patient data, identifying similar patient groups.
With all of these use cases, there is obviously a crucial role for trials and having professionals ‘in the loop’, and a debate to be had about the potential for bias, accuracy, privacy and overall patient experience. But with big investments being made, these debates about generative AI and its implications for the healthcare industry are set to continue. Bio-tech company Insilico Medicine has also published findings on how it is using generative AI to design new molecular structures that can target proteins which contribute to disease progression.
Medical training and simulations
Surgeons can use these simulations to virtually practice complex procedures, evaluate different approaches, and anticipate potential challenges. By optimizing surgical planning, generative AI improves surgical outcomes and patient safety. Generative AI plays a crucial role in augmenting telemedicine and remote patient monitoring, especially in the era of remote healthcare delivery.
That is, plenty of ways to utilize their AI platforms in bespoke applications their customers have to build. “We’re still five minutes into the marathon,” Gartner analyst Chirag Dekate says of the healthcare AI landscape. DocumentationPatient-doctor interactions during consultations generate a load of manual process work, particularly transcribing these conversations into EHR fields and coding them appropriately. This taxes already overworked medical professionals and is often blamed for elevated professional burnout rates.
Generative AI has the potential to revolutionize healthcare by enabling the creation of synthetic data for training models, generating personalized treatment plans, and assisting in medical research and drug discovery. In healthcare, generative AI tools exceed human capacity by drawing from vast knowledge and reviewing extensive medical literature, studies, and clinical outcomes. Its various benefits include early disease detection, personalized medicine, and improved healthcare plan enrolment. By examining data and trends, generative AI improves diagnosis, treatment strategies, and patient outcomes. Generative AI, a type of artificial intelligence, can transform ordinary inputs into extraordinary outcomes.
The average hospital is said to produce about 50 petabytes of data every year, which adds up to approximately 12.5 trillion digital copies of the King James version of the Bible. What’s more, the volume of data generated in healthcare is reportedly increasing by 47% per year, a significant clip for any industry. With generative AI, creating communications that resonate with people is really important, and that’s something that is going to help with payer-provider communications. But it’s also going to be a tremendous asset to the communications that healthcare organizations are having with consumers.
Helpful tips on using Generative AI in healthcare
By automating the generation of medical reports, generative AI reduces the time and effort required for documentation, allowing healthcare professionals to focus more on direct patient care. This not only improves workflow efficiency but also contributes to better patient outcomes. Generative AI can accelerate the drug discovery process by designing novel molecules and compounds with desired properties. By exploring vast chemical spaces, these models can generate potential drug candidates, saving time and resources in the initial stages of drug development.
The CEO of Providence Health Plan visits the Payer’s Place and addresses the future of payment models. “Now, if we think about where the market is headed, I think we will see some more consolidation in the future as these vendors Yakov Livshits try to become a one-stop-shop for these different EHR features. And I think we can also expect to see legacy incumbents, like Epic, add more generative AI capabilities to address these functions,” Komatireddy declared.
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.
To prevent this, conducting a thorough cost-benefit analysis is essential to ensure that investments in generative AI genuinely enhance patient outcomes and operational efficiency. For example, researchers at the University of Toronto utilized generative diffusion techniques, similar to tools like DALL-E, to design previously unknown proteins. With such breakthroughs, generative AI is poised to streamline and enhance the drug development journey. The integration of AI applications with smart devices like smart bands allows for real-time monitoring of a patient’s heart rate. Ensuring strict data integrity and employing robust security measures are vital in preventing such misuse and upholding the ethical use of generative AI in healthcare and beyond.
Recruiting and retaining patients for trials is difficult because it requires specific inclusion and exclusion criteria that have to be analyzed across various data sets. LLMs can summarize interactions between sales representatives and healthcare professionals (HCPs) through phone and email transcripts with healthcare providers, suggesting the next-best step. Improved patient / member engagement
Chatbots and virtual assistants are deployed on a healthcare organization’s website or mobile application, providing an interactive, real-time way to enhance patient communication and guidance.
Healthcare patient engagement: Imagining a better, bolder future
Then, the AI system can aid medical professionals by providing ongoing summaries of the patient’s condition. This allows doctors to focus on prescribing the appropriate treatment instead of being engaged with administrative work. Research for new drugs requires medical scientists to canvas voluminous data for exploring new medicines and their potential side effects. Rather than undertaking the task manually, researchers can train generative AI models with existing drug datasets. Then the model can generate new drugs with unique molecular combinations and simulate their efficacy.
- On the back of this has also come Med-Palm 2 – an AI that has been specifically developed for the healthcare industry and is trained to answer medical questions.
- Generative AI can support tele-diagnosis by analyzing patient data, medical images, and symptoms.
- For the healthcare industry, this means limiting data inputs to FAQ pages, CSV files, and medical databases – among other internal sources.
- LLMs can summarize interactions between sales representatives and healthcare professionals (HCPs) through phone and email transcripts with healthcare providers, suggesting the next-best step.
Training generative AI model requires storing and processing large numbers of sensitive medical data. Developers, service providers, and medical institutions must implement measures to safeguard patients’ privacy and comply with industry regulations. Generative AI’s main applications in healthcare often involve suggesting alternative treatment routes or medical solutions based on identified patterns, leading to profound ethical concerns. Data-driven intelligent solutions can be implemented to manage these issues effectively.
This will generate a grid image of molecules and their corresponding penalized logP values. MarketsandMarkets is a competitive intelligence and market research platform providing over 10,000 clients worldwide with quantified B2B research and built on the Give principles. It’s evident that Google is making concerted efforts to establish a strong foothold in the Healthcare AI sector through its innovative solutions, partnerships, and investments.
A study published in the journal PLOS Computational Biology demonstrated the use of generative models to predict drug-protein interactions, facilitating the identification of new drug targets and minimizing the risk of adverse effects. A study published in NCBI reports utilized generative models to optimize drug dosages for patients with Parkinson’s disease. A study published Yakov Livshits in JAMA Network Open utilized generative models to simulate diverse patient populations for cardiovascular disease clinical trials. Generative AI techniques can generate synthetic patient records, which can be used to augment real-world patient data for research. This expands the dataset size, leading to more comprehensive analyses and potentially uncovering new insights.
In this context, explainability refers to the ability to understand any given LLM’s logic pathways. In other words, in an industry like healthcare, where lives are on the line, the stakes are simply too high for professionals to misinterpret the data used to train their AI tools. When developing GenAI systems for healthcare, it’s essential to train the model with high-quality medical datasets. This means collecting, cleaning, and labeling medical data such as imaging results, lab tests, and patient records with stringent guidelines.