The promising future of Generative AI in Healthcare: Use Cases and Insights

  • Last updated on: September 16, 2024
The role that technology has been playing in enabling the healthcare industry to cope with future uncertainties, particularly post-COVID-19, has been phenomenal. From the early days of computing to the latest advancements in artificial intelligence, healthcare has consistently been at the forefront of embracing technological progress. 

One such recent technological advancement that the health industry is keen to try is generative AI. The medical fraternity is curiously evaluating the potential of generative AI in revolutionizing healthcare in the years ahead. Researchers are exploring various use cases of generative AI, working out ways in which its capability of producing natural and human-like responses to the inputs provided can be tapped to transform care delivery.

In this article, we delve deep into how generative AI can create positive value for every stakeholder in the healthcare delivery chain when leveraged appropriately. 

Table of Contents

Gen AI use cases In Healthcare

A Few Potential Healthcare Use Cases of Generative AI:

From helping practitioners offer more effective treatments to patients and empowering patients to self-manage their chronic conditions to automating administrative tasks to advancing clinical research, the opportunities to employ generative AI in healthcare appear endless. Following are a few promising applications of Generative AI in the healthcare space:  

1. Personalized Patient Engagement

Imagine a healthcare system where there is an assistant who knows you better than a friend. Generative AI enables you to do just that. It seamlessly integrates into new-generation EHR systems as an intelligent virtual assistant, offering personalized interactions and quick responses to frequently asked questions. This takes patient engagement to a whole new level. In the mental and behavioral health segment too, AI-powered virtual agents can be used to generate meaningful conversations and therapeutic content in the form of art, music, and writings to enhance patient engagement. 

2. Intelligent Triage and Decision Support

Generative AI can help in the management of triages because of its ability to ask relevant questions and provide preliminary diagnoses. By rapidly matching patient responses with accepted medical guidelines, the system can simplify the process of triage. This can be of great advantage during a medical emergency where every second counts. The technology can offer health professionals insightful information in just a few seconds for proactive and prompt medical intervention.  

3. Medical Coding and Documentation

With over 70,000 codes in the existing ICD glossary, the task of medical coding is difficult to achieve manually. Generative AI eases the complexities of manual medical coding. It automatically analyzes the medical documentation to extract relevant medical codes and suggest appropriate code matches. This significantly reduces the administrative burden and errors, leading to smoother billing and reimbursement processes that improve patient experience.  

4. Automated Appointment Scheduling

Missed appointments result in huge financial losses for healthcare providers. Generative AI addresses this challenge by making appointment scheduling seamless. With its natural language understanding capability, Gen AI is far more receptive to patient preferences, which improves patient engagement and minimizes the challenge of patient no-shows. Additionally, automation allows better resource utilization, improving cost savings, operational efficiencies, and patient experiences.  

5. AI-powered Medical Imaging

Gen AI is showing transformative potential in medical imaging. Diagnostic precision is enhanced phenomenally with gen AI refining X-rays, MRIs, and CT scans. It does this by filling in missing information and reducing image noise and goes a step further by generating synthetic images for training algorithms, automating radiology report generation, and assisting pathologists in anomaly detection in histopathology images.  

6. Chronic Disease Management

Generative AI enhances wearable devices and remote patient monitoring, functioning as a versatile virtual health assistant. Through natural conversations, it delivers personalized recommendations for lifestyle adjustments and medication adherence for patients suffering from chronic ailments. This technology bridges patient-provider communication gaps, facilitating ongoing patient support and improving rehabilitation and recovery efforts. Generative AI also shines in designing personalized prosthetics and orthotics for people suffering from physical deformities. 

7. Language Translation and Collaboration

Language barriers can hinder effective communication between a patient and a health professional. Generative AI acts as a reliable language translator, effectively breaking down language gaps in patient-provider interactions. This ability of Gen AI also enables seamless communication across language-diverse teams to solve various medical complexities. 

8. Telehealth Improvements

Telehealth has rapidly gained acceptance worldwide, especially post the pandemic. Generative AI infuses a whole new lifeblood into the working of telehealth systems, enabling intelligent diagnosis and enhancing patient outcomes. AI-powered telehealth supports the real-time availability of vital data related to patients, such as pulse rate and SpO2 levels, helping medical professionals monitor patients remotely with greater ease. The technology’s multilingual capability again comes into play here, enabling effective communication between patients and providers from across geographies. 

9. Medical Research and Drug Development

Generative AI accelerates clinical research by automating data mining and literature reviews for medical researchers. It efficiently identifies critical gaps in knowledge, equips researchers with insights for hypothesis generation and experimental design, and generates synthetic patient data. Additionally, it bolsters advancements in genomic research by refining genomic analysis techniques and generating artificial genomic data. The technology also speeds up drug development by analyzing chemical structures and optimizing lead compounds.  

10. EHR Document Intelligence for Medical Billing

Generative AI revolutionizes medical billing processes by efficiently processing massive amounts of unstructured data within Electronic Health Record systems. It intelligently extracts and interprets important clinical information from large data sets, such as diagnoses, procedures, medications, and medical codes. This capability accelerates billing processes and minimizes the possibility of claims denials due to errors, resulting in a better billing experience. 

11. Medical Comorbidities Finder

Generative AI is proving to be a priceless tool in detecting potential comorbidities that may otherwise go unnoticed with a focus on just the primary diagnosis. 

It does this by analyzing the patient’s full medical history including medications and lab test results to find hidden patterns that may suggest the presence of some underlying illness. This helps clinicians make a more holistic diagnosis based on the full picture of a patient’s health status. Addressing the comorbidities early can reduce the risks of future health complications, improve patient outcomes, and potentially lower healthcare expenses in the long run. 

12. Healthcare Contact Center Intelligence

Generative AI is transforming healthcare contact centers through intelligent virtual agents that understand and respond to a patient’s queries in natural language. The technology automates routine tasks like setting appointments, refilling prescriptions, and answering basic medical inquiries, freeing up human agents to deal with more complex and sensitive issues. This increased efficiency enhances patient satisfaction while improving cost-effectiveness. Also, the 24/7 availability of a virtual agent gives patients access to care and support whenever they need it, further contributing to the positive patient experience. 

Looking Ahead: Gen AI is Rapidly Evolving, Yet Nascent

While Gen AI does show immense potential in revolutionizing the healthcare industry, the technology is still developing and needs much more testing and training for newer use cases. It also comes with its set of limitations and ethical challenges. One needs to remember that everything that generative AI can do is only as good as its training data, and the biases in that data can result in inaccurate or unreliable responses.

Also, most generative AI tools are statistical models that respond to trends and patterns; they lack any emotional quotient and hence are inefficient in dealing with big or unusual medical cases. Hence, at no point, generative AI can be used to replace the judgment and expertise of a qualified healthcare professional. Instead, it can be tried as an accompanying tool to obtain better patient outcomes, provided it is leveraged correctly, for the right purposes, and under proper supervision.  

The future of Gen AI in healthcare is undoubtedly promising, as this intelligent technology is steadily finding its way into healthcare through diverse applications and new use cases every day. By staying at the forefront of this Gen AI revolution, KANINI is committed to empowering healthcare organizations with innovative Gen AI solutions that create better value in care. Connect with us to explore new opportunities in this space. 

Author

Anand Subramaniam

Anand Subramaniam is the Chief Solutions Officer, leading Data Analytics & AI service line at KANINI. He is passionate about data science and has championed data analytics practice across start-ups to enterprises in various verticals. As a thought leader, start-up mentor, and data architect, Anand brings over two decades of techno-functional leadership in envisaging, planning, and building high-performance, state-of-the-art technology teams.

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