In the continually shifting landscape of human-machine intelligence collaboration, the introduction of OpenAI’s ChatGPT has reignited enthusiasm for artificial intelligence (AI). Businesses are incorporating AI at varying rates, guided by factors such as their strategic objectives, resource availability, expertise and the expanding utilization of AI in diverse applications and services as well. The level of data quality required by AI varies based on the specific application.
However, in scientific domains such as pharmaceuticals and life sciences, the stakes are considerably higher. Low-quality data can be a matter of life and death and these fields often grapple with data scarcity. Unlike industries with abundant data, finding the necessary information to discover new drug molecules can be quite challenging. A strong partnership between humans and machines has emerged.
Human experts play an important role in providing context and additional information to AI algorithms in scientific research. This collaboration helps refine algorithms and ensures their accurate use within various workflows. However, there are challenges to overcome. AI’s ability to analyze vast amounts of data is impressive but context is often not explicitly stated in scientific research papers. Scientists provide the essential context and guide AI systems through rewards for positive outcomes and corrections for errors.
While some highly educated scientists initially hesitate to collaborate with AI due to research complexity and costs, they eventually realize the efficiency and precision AI brings. However, trust remains an obstacle. Calls for AI transparency are growing to build trust among scientists and auditors.
Moreover, the incorporation of AI into fields like pharmaceuticals is strongly influenced by the stringent regulations in these sectors. Balancing technological advancements with regulatory compliance is important. Organizations must ensure their technology is both safe and accurate as AI lacks inherent governance, privacy and security measures.
