Where is life sciences AI heading? More is better when it comes to Big Data and machine learning. This is particularly true in the fields of medicine and pharma. A report by Accenture estimates that by the year 2026, Big Data in conjunction with machine learning in medicine and pharma will be generating value at a prodigious rate: $150 billion/year.
This figure reflects how the tools of artificial intelligence (AI) are expected to help doctors, patients, insurers, and overseers reach better decisions, optimize innovations, and improve research and clinical trial efficiency.
Healthcare data comes from myriad sources: hospitals, doctors, patients, caregivers, and research. The challenge is putting all the data together in a compatible format and using it to develop better healthcare networks and protocols. This is where machine learning comes in.
The main purpose of machine learning applications specific to medicine and pharmacotherapy is to make data accessible and usable for improving prevention, diagnosis, and treatment as a matter of course. Pioneers in medicine and pharma machine learning are already addressing some key areas progressing life sciences AI applications.
Machine Learning Applications in Medicine and Pharma
This article is informed by a TechEmergence analysis of AI initiatives undertaken by the five largest global drug makers. Whereas the analysis presents a broad survey, covering all the major trends of industry applications in life sciences and biotech, this article is more focused. It emphasizes six of the trends that TechEmergence believes will be most meaningful in the near term including:
- Diagnosis and Disease Identification
- Personalized Medicine
- Drug Discovery and Manufacture
- Clinical Trials
- Radiotherapy and Radiology
- Electronic Health Records
