Artificial intelligence (AI) is fast emerging as a force that is reshaping society and revolutionizing a myriad of industries. Healthcare, in particular, is seeing remarkable advancements with the integration of artificial intelligence, and more specifically, Generative AI, a groundbreaking technology with the potential to transform all aspects of healthcare, including pharma, medical research, and even health insurance.

Generative AI focuses on creating algorithms capable of generating new content, such as images, text, or even entire virtual environments, in a way similar to how humans create. With its ability to simulate complex medical scenarios, Generative AI has opened new horizons for diagnosis, drug discovery, and personalized treatment plans, along with the enhancement and generation of medical imaging, virtual medical assistants, and more.

These technology advancements have proven to be a transformative force in the healthcare industry, promising significant advancements in medical research, diagnostics, and patient care. However, to fully realize the potential of Generative AI, the integration of human expertise is critical. The synergy between Generative AI and human input holds immense promise across the value chain, including pharmaceutical companies, healthcare providers, doctors, patients, and the insurance industry.

How generative AI in healthcare integrates the human touch
By training and enhancing models through human feedback, particularly for Large Language Models (LLMs), models are now comprehending medical literature, records, imagery, and generating personalized treatment recommendations. However, to ensure their accuracy and reliability, LLMs require continuous refinement and guidance. Human feedback plays a vital role in training data creation, data curation, filtering biases, and aligning generated content with industry standards, resulting in more precise and improved diagnostic accuracy.

Applications and the emergence of reinforcement learning from human feedback (RLHF) in healthcare
Once an AI model is deployed – the work does not stop. On top of a fine-tuned model’s ability to continuously improve, the human layer maintains a steady beat of reinforcement to make the model smarter over time. This is where “reinforcement learning from human feedback” or RLHF comes in. RLHF is a subfield of Reinforcement Learning (RL) that involves incorporating feedback from human evaluators and a reward system to improve the learning process.