“Our intelligence is what makes us human, and AI is an extension of that quality.“ Dr. Yann Lecun, Professor, New York University
In the spirit of the new year, here are a few of my predictions for this year for AI in medicine and healthcare (five predictions here and another five predictions will be included with the next newsletter):
- The era of cognitive neuroscience in AI will begin this year and will be an important dimension of AI in medicine this decade. Cognitive architecture elements such as attention, consciousness, metacognition, abstraction, rules, concepts, and memory will be increasingly more incorporated into AI.
- Data from wearable devices will demand an AI strategy as this data will be otherwise much less effective in healthcare. The data “tsunami” from the increasing number of wearable devices will need to have an AI solution such as embedded or edge AI (machine learning + internet of things) to be relevant in the overall disease management in the future.
- Generative methodologies such as generative adversarial networks (GANs) will help neutralize the problem of inadequate healthcare data. A major issue is lack of healthcare data (including access) for AI projects, but this deficiency can be partly neutralized by generative AI methods that will create synthetic data (although heterogeneity of data may be an issue).
- AI in the form of robotic process automation (RPA) will become more appreciated as a useful tool in healthcare administration. As many tasks in healthcare administration are repetitive and susceptible to human errors, these tasks are amenable to AI tools such as robotic process automation and machine learning.
- Conversational AI will be increasingly more common and sophisticated in healthcare. With advances and improvements in natural language processing as well as in machine and deep learning, conversational AI (messaging apps, speech-based assistants, and chatbots) is a much needed AI tool in disease management in healthcare.
