Generative AI applications for the healthcare ecosystem: Opportunities and challenges

Inderpreet Kambo, Principal Partner, IQVIA stresses that in healthcare, GenAI has the potential to revolutionise diagnosis, treatment, and disease management. It can transform the sector by creating new data, predicting outcomes, and optimising processes

Generative AI (GenAI) is a cutting-edge technology that leverages advanced algorithms to create new data and content based on patterns learned from existing information. Imagine GenAI as a highly intelligent system that can generate text, images, and predictions by analysing large datasets and understanding underlying patterns. Generative AI uses smart algorithms to create new content from existing information. Unlike traditional AI, which follows set rules, GenAI can produce text, images, and other data by learning from large amounts of information.

Generative AI and its applications in healthcare

In healthcare, GenAI has the potential to revolutionise diagnosis, treatment, and disease management. It can transform the sector by creating new data, predicting outcomes, and optimising processes. However, its benefits depend on effective implementation. The true impact of GenAI will be shaped by how well we integrate it into current practices, address challenges, and leverage its capabilities. We will explore how GenAI can shape the healthcare ecosystem, highlighting its significant opportunities and potential challenges in the following annotation.

Below are some of the most mature and commonly used applications of Generative AI in healthcare today.

1. Drug discovery and development: GenAI speeds up drug discovery by predicting molecule interactions and finding new drug candidates, saving time and cost.

2. Personalised medicine: By analysing patient data, including genetic information, GenAI can help create personalised treatment plans. This approach enhances treatment efficacy and reduces adverse effects.

3. Medical imaging and diagnostics: GenAI boosts diagnostic accuracy by analysing medical images and spotting patterns often missed by humans, enabling earlier disease detection and better clinical decisions.

4. Clinical decision support: GenAI aids clinicians by providing evidence-based recommendations and highlighting relevant patient data. This enhances decision-making, reduces errors, and boosts clinical efficiency.

5. Operational efficiency: GenAI streamlines healthcare operations by automating administrative tasks and optimising resource management. This results in cost savings and improved patient care.

While Generative AI presents exciting possibilities, it also comes with challenges. Handling sensitive patient data requires strict adherence to data privacy and security regulations like the Digital Personal Data Protection Act (DPDPA). Ensuring robust data protection and maintaining privacy are crucial to building and sustaining trust. Additionally, GenAI models can inherit and amplify biases present in their training data, potentially exacerbating healthcare disparities. To address this, it’s important to use diverse datasets and develop algorithms that can detect and correct biases. Many GenAI models also function as “black boxes,” making their decision-making processes difficult to understand. In India, where technology adoption can vary, enhancing model transparency and providing adequate training for healthcare professionals is essential. Finally, as the regulatory landscape for AI in healthcare evolves, it’s critical to comply with emerging guidelines and address ethical concerns to ensure responsible and effective use of AI.

Conclusion

Generative AI offers significant potential for advancing healthcare and life sciences, providing innovative solutions to various challenges. To fully realise these benefits, stakeholders must address concerns related to data privacy, bias, interpretability, and regulatory compliance.

To leverage Generative AI effectively in the Indian healthcare system, focus on these key actions: Enhance data governance by implementing strong privacy and security measures per the Digital Personal Data Protection Act (DPDPA). Address bias and fairness by using diverse datasets and developing algorithms to minimise biases. Improve interpretability by making AI models more transparent and providing training for healthcare professionals. Engage with regulators to ensure compliance with evolving guidelines and address ethical concerns. These steps will help optimize the benefits of GenAI while managing potential challenges.