“Artificial intelligence (AI) is quickly becoming a revolutionary and game-changing tool in modern oncology, with promising uses in early diagnosis and drug discovery. Machine learning (ML), deep learning (DL), reinforcement learning (RL), natural language processing (NLP), and generative models are some of the AI methods that are becoming very important for cancer care. With an emphasis on early diagnosis, mutation mapping, and drug design, this article aims to review the existing literature and investigate the role of AI technologies in oncology.”
AI impact on vaccine development
AI accelerates vaccine development by streamlining antigen selection and immunogen design.
Machine learning predicts antigenic epitopes and assesses immunogenicity.
Generative models and molecular dynamics enhance immunogen stability and coverage.
There’s basically no evidence they increase productivity.
If AI can make your job easier, you probably don’t have a real job.
There’s a statement based on nothing.
source: http://www.nber.org/papers/w34836
The study you cited is basically asking senior executives what they predict the impact of AI will be. Not exactly convincing empirical evidence.
Try these search terms
AI impact on Cancer research
https://www.nature.com/articles/s41698-026-01276-6
“Artificial intelligence (AI) is quickly becoming a revolutionary and game-changing tool in modern oncology, with promising uses in early diagnosis and drug discovery. Machine learning (ML), deep learning (DL), reinforcement learning (RL), natural language processing (NLP), and generative models are some of the AI methods that are becoming very important for cancer care. With an emphasis on early diagnosis, mutation mapping, and drug design, this article aims to review the existing literature and investigate the role of AI technologies in oncology.”
AI impact on vaccine development
AI accelerates vaccine development by streamlining antigen selection and immunogen design.
Machine learning predicts antigenic epitopes and assesses immunogenicity.
Generative models and molecular dynamics enhance immunogen stability and coverage.
There are thousands of other studies.