AI
Artificial intelligence (AI) has been defined by some as the "branch of computer science dealing with the simulation of intelligent behavior in computers" , however, the precise definition is actually a matter of debate among experts. An alternative definition is the branch of computer science dedicated to creating algorithms that can solve problems without being explicitly programmed for all the specificities of the problems. AI algorithms and in particular deep learning (part of machine learning) aim to either assist humans with solving a problem or solve the problem without human input. The exponential increase in computational processing and memory capability has opened up the potential for AI to handle much larger datasets, including those required in radiology.
The term AI encompasses numerous specific areas and approaches, including:
- computer-aided diagnosis/detection (CAD)
- machine learning
- natural language processing
- rule-based expert systems
- radiomics
- reduction of noise (noise reduction) and optimization of image acquisition
Ethical Issues
The rapid advancement of AI in medical imaging has identified a number of opportunities and challenges for maturing the field towards building robust and reliable infrastructure.
- issues of data governance
- data ownership
- data sharing and exchange
- data privacy
- data bias
- data quality and establishing ground truth
- issues of algorithms
- transparency
- algorithm bias
- issues of AI in radiology practice
- liability
History and etymology
The term artificial intelligence is credited to John McCarthy, a mathematician (and the creator of the LISP programming language) who proposed and organized a summer research conference that happened in 1956 at Dartmouth on artificial Intelligence, who used the term. The conference is considered by many to be the moment that AI was founded as an area of academic research, however, it could be argued that the creation of the field began earlier with Alan Turing, who developed the Turing test, or even before.
