News & Press
By Dr. Matthieu Giraud, Director, Global Peptides, Lipids & Carbohydrates Platforms
Speciality Chemicals Magazine, April 2019
The term Artificial Intelligence (AI) refers to a well-defined field of research, which seeks to understand how human cognition works, reproduce it and thereby create cognitive processes comparable to those humans. The field is vast, both in terms of the technical procedures used and the disciplines involved. AI methods employ many algorithms that were developed several decades ago. They are numerous and diverse – including:
Machine learning has brought a new era of hope for AI of late. Thanks to new algorithms, more data sets and increased computing power, applications are being used in many areas such as translation, autonomous car control, and cancer detection. It is developed in a marked technological context by datafication, robotics, blockchain, supercomputing and massive storage. These different technological realities will surely pave the way for the future of AI.
The most emblematic AI tool is IBM’s Watson computer software, which was introduced in 2005 into the healthcare market in 2005 and has been used, notably at the Memorial Sloan Kettering Cancer Centre, in diagnostic assistance and therapeutic proposal. This type of software synthesises information from millions of medical reports, patient records, clinical tests and knowledge from medical research to support decision-making.
Some software may soon diagnose cancer as well as, or even better than, specialists. According to a recent study, AI has been able to detect breast cancer with a success rate of 92%, almost equivalent to what specialists (96%) can reach.1 When combined with the physician’s analysis and diagnostic methods from automated software, the success rate is 99.5%, with a greatly reduced error risk.
The health sector is a knowledge-intensive industry, which depends on data and analytics to improve therapies and practices. There has been tremendous growth in the range of information being collected, including clinical, genetic, behavioural and environmental data. Every day, huge amounts of data are generated from devices including electronic health records (EHRs), genome-sequencing machines, high-resolution medical imaging, ubiquitous sensing devices and smartphone applications that monitor patient health.
At the same time, the potential to process and analyse these emerging multiple streams and large volumes of data, and to link and integrate them, is growing. Such data-driven innovation can yield many benefits, including new insights into the natural history of diseases, along with their diagnosis, prevention and the opportunity for further development of personalised therapies. Indeed, there is growing evidence that Big Data can be leveraged to transform healthcare.
The term ‘Big Data’ refers to the data being generated at an unprecedented rate, which is too vast and complex for traditional data management techniques to process. Big Data analytics deliver actionable insights based on information and raw data, acting as the driving force behind many ongoing waves of digital transformation, including AI. Its use is transforming many industries.