Figure 1. Pharmaceutical areas with an AI footprint
“… 60 use cases and 36 companies or institutions expanding their activities in different pharmaceutical areas.”
* Our review was limited to publications and corporate communications in 2019 and later, hence this is a non-exhaustive list of industry developments. If you would like to add a use case or company relevant to AI applications in the pharmaceutical domain, please do not hesitate to contact us at firstname.lastname@example.org
Drug Discovery and Development
“… biomarker discovery with data science tools that can ensure their sensitivity, robustness and validate results…”
Smart automation of processes clears up time to focus on more urgent or complex tasks (see the visual below). A clear example is automating patients’ reminders and follow-ups. This remote monitoring increases chances for timely interventions in a treatment plan when drugs lack efficiency and addresses drug adherence issues. Another example of operational optimization is reading, cleaning and analysis of structured and unstructured data such as patients’ medical records, doctor notes, prescriptions and detected side effects, etc. A continuous medical follow up history generates a multitude of personalized data that, if harvested effectively, can bring us closer to the notion of the Big Data and greatly advance medical research.
“A continuous medical follow up history generates a multitude of personalized data…”
“AI can make gene editing initiatives more accurate, safer and cheaper.”
While genome editing is far from being translated into therapies, researchers should use AI to minimize potential damaging off-target effects. Primarily, research institutions such as Broad Institute, MIT and Wellcome Sanger Institute investigate advancements in gene editing technology. There are a few developments in this area, where AI can make gene editing initiatives more accurate, safer and cheaper (see the visual below).
“Unsupervised learning could change the way diseases are classified, as current disease ontologies were created in a top-down fashion based on the observed symptoms.”
“The most common use case (based on the number of involved companies) is compound screening for their affinity and toxicity.”
Figure 2. Frequency graph of AI use cases attributed to pharma companies or academic institutions. Node size corresponds to the number of attributable interactions. Green nodes indicate use case names and red nodes correspond to different players on the market.
Disclaimer: The network depiction of the use cases and participating companies was limited to publications and corporate communications in 2019 and later, hence this is a non-exhaustive list of industry developments. If you would like to add a use case or company relevant to AI applications in the pharmaceutical domain, please do not hesitate to contact at email@example.com
- Shaywitz D. (2019) “Novartis CEO Who Wanted To Bring Tech Into Pharma Now Explains Why It’s So Hard”.
- Data Revenue (2019) “Artificial Intelligence in Medicine”.
- de Jesus A. (2019) “Artificial Intelligence in the Pharmaceutical Industry – An Overview of Innovations”.
- Yeadon N. (2019) “Artificial Intelligence in Pharma”.
- Conroy D., Conroy M. (2019) “AI in Pharmaceuticals”.
- Proffitt C. (2017) “Top 10 Artificial Intelligence Companies Disrupting the Pharmaceutical Industry”.
- Arsene C. (2019) “Artificial Intelligence & Pharma: What’s Next?”.
- PharmaTutor (2019) “TOP 10 Pharmaceutical industries using Artificial Intelligence (AI)”.
- Sennaar K. (2019) “AI in Pharma and Biomedicine – Analysis of the Top 5 Global Drug Companies”.
- Liu A. (2018) “Making CRISPR-Cas9 gene editing safer with artificial intelligence”.