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How Generative AI Will Revolutionize Medicine

written by valerie smoliakova Jan 19, 2024

Despite its recent emergence in medicine, generative artificial intelligence (AI) has the potential to completely transform healthcare and patient outcomes as we know them. The U.S. healthcare system, riddled with many complex issues exacerbated by the COVID-19 pandemic, is facing extremely high rates of labor shortage and physician burnout. Implementing generative AI can alleviate this problem by improving healthcare efficiency, patient and physician experience, and clinical outcomes. 

What is generative AI?

Stemming from new advancements in AI, generative AI separates itself from traditional AI, which can be used for simple analytical tasks. Generative AI is distinguished by its ability to analyze patterns and data to generate new content, such as text, images, and synthetic data. Some examples include OpenAI’s GPT-4 and Google’s Bard, which are both large language models intended to be used for conversational applications. 

How can generative AI be used in healthcare?

From being able to assist with clinical documentation to serving as a chatbot assistant for patients to inquire about health questions, the potential healthcare implementations of this technology are tremendous. Simply put, generative AI is designed to handle more complex and time-consuming tasks, which can include:

  1. Facilitating clinical documentation, cutting the time spent on patient documentation and financial paperwork. 

  2. Condensing complex medical information into easy-to-understand summaries for patients.

  3. When paired with speech recognition technology, it can automatically document and summarize patient visits, serving as a medical scribe.

  4. Analyzing complex data and results, providing a quicker and more accurate diagnosis, and helping physicians to effectively identify patterns.

  5. Analyzing an individual’s genetic background and lifestyle factors, providing a personalized medicine approach, and creating specialized treatment plans on a case-by-case basis. 

  6. Predicting disease progression or outcomes.

Biotechnology and software companies have already seized the opportunity to leverage the capabilities of generative AI. For example, DeepScribe, a San Francisco-based software company, offers an AI medical scribe that relies on speech recognition models to transcribe patient interactions and even extract health information for important medical documentation. 

Ultimately, using generative AI for tasks like automatic documentation and billing processes can reduce administrative costs, decrease the risk of errors, improve accuracy, and optimize tasks for staff. 

Implications for electronic health records:

One of the biggest implications this technology will have is tied to electronic health records (EHRs), which studies have shown to be correlated with the high turnover rate among healthcare workers. 

EHRs are maintained by healthcare providers and are known to take about 16 minutes to complete for each patient, culminating in around 3-6 hours every day. Though important for improving patient outcomes and identifying patterns, physicians’ responsibility for maintaining EHRs can degrade the patient-physician experience and affect healthcare workers' cognition. 

This is where generative AI comes in; with simple substitutions, it can significantly reduce the time physicians spend on tedious paperwork and digital clinical documentation. This means that, not only will physician productivity and focus on the patient improve, but also patients will experience reduced wait times and quicker access to important health information. 

Drug Discovery and Development:

Generative AI will have significant impacts on drug discovery and research as well. The process of drug discovery and development is time-consuming, expensive, and inefficient. Certain biotechnology companies are already implementing the technology to optimize drug discovery and improve scientific modeling. With generative AI, researchers will be able to scan large sets of biological data and quickly identify potential drug candidates. 

Insilico Medicine, a biotechnology company focused on developing drugs for cancer and other serious illnesses, has transformed into a generative AI-driven company. They leverage the technology to accelerate disease target identification, generate novel molecule data, and predict clinical trial outcomes and data. 

Societal Implications: 

The benefits brought by generative AI can have a significantly positive impact on the social needs of patients and certain marginalized groups. By achieving more efficient healthcare delivery, lowering administrative costs, and providing personalized treatment plans, both the accessibility and affordability of healthcare services may improve for a wider range of the population. 

It is important to mention that while the future of healthcare with generative AI looks promising, there are some concerns about the potential pitfalls of this technology. A major concern is the ethical implications surrounding patient data privacy and security. 

Researchers have advocated for regulatory oversight if generative AI is used in conjunction with patient data and sensitive information. The potential for cybersecurity attacks indicates a need to protect patient privacy from data breaches. Ensuring a strong security system and compliance with ethical guidelines, such as Health Insurance Portability and Accountability Act (HIPPA), will be vital in protecting patient’s personal information. 

Additionally, being a large language learning model (LLM) that scans massive data sets, there is a possibility of social bias in much of this produced data. Biased data, combined with the technology’s dynamic nature and adaptable response capabilities, may perpetuate health disparities by providing biased results, treatment plans, or responses. By prioritizing a human-centered approach and using AI to enhance, rather than replace, a physician’s role, potential healthcare disparities or discrimination can be prevented. 

The Next Steps:

Generative AI is gradually being implemented in some administrative sectors of healthcare, but it is still far from being fully embraced in the medical world. However, AI technology is advancing and improving every day, with dozens of biotechnology startups emerging. As we continue to explore this transformative landscape, stakeholders, healthcare professionals, and the public must prioritize engaging in ongoing conversations and addressing ethical concerns. Ensuring a collaborative effort with a human-centered approach will undoubtedly support the stable integration of generative AI. 

 

Written by Valerie Smoliakova

Edited by Mayasah Al-Nema.

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References:

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