The Transformative Potential of Generative AI in Healthcare
AI has made significant contributions to healthcare, improving patient care, advancing research, reducing costs, and enhancing accessibility to healthcare services. Ashutosh Khare, the Centre Leader of Cigna in India, highlighted the transformative potential of generative AI in the healthcare sector during the Cypher2023 event, the largest AI conference in India.
Cigna Group, based in Bloomfield, Connecticut, is a global organization that serves 200 million customers across 30 different countries. While its main source of revenue comes from the US market, Cigna also has a Global Capacity Centre (GCC) in Bengaluru, India. The company plans to hire approximately 1000 people in the next two years.
Healthcare Chatbots
One area where generative AI can have a significant impact is in the development of healthcare chatbots. Cigna is working on creating a comprehensive chatbot system that will handle millions of agreements with vendors, agencies, pharmacies, and clinical companies in the United States. This chatbot will also be integrated into an Interactive Voice Response (IVR) system, enabling it to efficiently respond to inquiries. Additionally, Cigna is developing a medical chatbot that streamlines insurance-related queries, providing quick and accurate answers about eligibility for specific medical treatments or interventions.
Disease Diagnosis
Another important application of generative AI in healthcare is disease diagnosis. Cigna is actively working on developing AI algorithms capable of diagnosing diseases from various medical imaging data, including images of skin diseases. These algorithms analyze patterns in the data to identify potential diseases and aid in early detection.
Drug Discovery
Generative AI is also revolutionizing the field of drug discovery. By analyzing a wide range of data sources, such as medical records, wearable device data, and genetic information, AI algorithms can identify new drug molecules that offer potential solutions for various ailments. Cigna uses a fine-tuned version of the Bard language model called BioBard specifically for clinical research and drug discovery.
Managing Healthcare Costs
Healthcare costs pose a significant challenge in the industry, and generative AI can help optimize medical coding and billing practices. By ensuring the accurate submission of insurance claims, AI can reduce claim denials and payment delays. It also assists in managing healthcare costs by minimizing unnecessary medical tests and optimizing provider contracts.
Ethical Concerns
With the increasing presence of AI in healthcare, ethical considerations become crucial. Transparency, privacy, and data security are key concerns that must be addressed. It is essential for the decision-making process of healthcare AI to be transparent and explainable. Patients should have control over their health data and be fully informed about its usage. Cigna, like other healthcare companies, recognizes the importance of maintaining a human touch in healthcare. AI should complement human expertise rather than replace it, with the goal of achieving equity and accessibility in healthcare while upholding ethical practices and accountability.
Looking ahead, Cigna has identified six specific use cases to focus on in the current year, with plans to expand to 15 in the following year. Khare emphasizes the importance of making progress by implementing two to three generative use cases within six months.
Editor Notes
The potential impact of generative AI in healthcare is vast, as demonstrated by Cigna’s initiatives. By leveraging AI algorithms, healthcare providers can enhance patient care, streamline processes, and improve cost management. However, it is crucial to address ethical concerns and ensure transparency, privacy, and data security in the implementation of AI systems. As the healthcare industry continues to evolve, the collaboration between AI and human expertise will play a pivotal role in delivering equitable and accessible healthcare services.
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