AI and healthcare: There seem to be almost endless ways artificial intelligence (AI) could transform healthcare, such as preventing disease and improving diagnoses and treatment, but for pharma there is a very real, near-term issue to solve – the escalating price and dwindling returns on R&D.
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There seem to be almost endless ways artificial intelligence (AI) could transform healthcare, such as preventing disease and improving diagnoses and treatment, but for pharma there is a very real, near-term issue to solve – the escalating price and dwindling returns on R&D.
The AI revolution is being driven by massive leaps forward in computing power and an increase in access to health-related data sets that have made AI a powerful mining tool, able to identify patterns and relationships between data points that might never be identified by human researchers.
For pharma, much of the near-term excitement about AI stems from its potential to improve drug discovery and development, not least because using the technology promises to liberate researchers from the constraints of ‘perceived wisdom’ based on prior experience and expectations, unlocking new treatment pathways.
According to a Deloitte report, AI ‘can help analyse large data sets from sources such as clinical trials, health records, genetic profiles and preclinical studies; within this data, it can recognise patterns and trends and develop hypotheses at a much faster rate than researchers alone.’
Deloitte also suggests AI can help improve study design and decision-making in clinical trials, improving recruitment and enrolment, adherence and real-time clinical trial monitoring, which are all expensive parts of the drug development process. And applying AI to real-world evidence – another hot topic in pharma – could help companies predict performance of certain trial sites, anticipate drop outs and even predict outcomes.
With the cost of bringing a new drug to market now approaching $2bn – mainly due to a high attrition rate – anything that can improve the efficiency of R&D is being welcomed by the pharma industry. Pfizer is using IBM’s Watson machine-learning platform to help find new immuno-oncology targets and drugs, for example, while Sanofi is using a rival system from UK firm Exscientia in a metabolic disease programme. Roche’s Genentech unit is collaborating with GNS Healthcare on a cancer project, and GlaxoSmithKline recently signed an AI-based drug discovery alliance with Cloud Pharmaceuticals.
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Investments like these are helping to drive explosive growth in the health AI sector. Recent market research from Global Market Insights (GMI) suggests that from a fairly modest level of $760m in 2016, the market will surge in the following eight years, growing at more than 40% per annum to top $10bn in 2024.
Pharma researchers are using the technology more and more for labour-intensive tasks like target identification, drug discovery and design and compound screening. And the technology is allowing the discovery process to be turned on its head, using patient-driven biological data to uncover new targets, rather than relying on the conventional trial-and-error approach.[…]
read more – copyright by www.pmlive.com
AI and healthcare: There seem to be almost endless ways artificial intelligence (AI) could transform healthcare, such as preventing disease and improving diagnoses and treatment, but for pharma there is a very real, near-term issue to solve – the escalating price and dwindling returns on R&D.
copyright by www.pmlive.com
The AI revolution is being driven by massive leaps forward in computing power and an increase in access to health-related data sets that have made AI a powerful mining tool, able to identify patterns and relationships between data points that might never be identified by human researchers.
For pharma, much of the near-term excitement about AI stems from its potential to improve drug discovery and development, not least because using the technology promises to liberate researchers from the constraints of ‘perceived wisdom’ based on prior experience and expectations, unlocking new treatment pathways.
According to a Deloitte report, AI ‘can help analyse large data sets from sources such as clinical trials, health records, genetic profiles and preclinical studies; within this data, it can recognise patterns and trends and develop hypotheses at a much faster rate than researchers alone.’
Deloitte also suggests AI can help improve study design and decision-making in clinical trials, improving recruitment and enrolment, adherence and real-time clinical trial monitoring, which are all expensive parts of the drug development process. And applying AI to real-world evidence – another hot topic in pharma – could help companies predict performance of certain trial sites, anticipate drop outs and even predict outcomes.
With the cost of bringing a new drug to market now approaching $2bn – mainly due to a high attrition rate – anything that can improve the efficiency of R&D is being welcomed by the pharma industry. Pfizer is using IBM’s Watson machine-learning platform to help find new immuno-oncology targets and drugs, for example, while Sanofi is using a rival system from UK firm Exscientia in a metabolic disease programme. Roche’s Genentech unit is collaborating with GNS Healthcare on a cancer project, and GlaxoSmithKline recently signed an AI-based drug discovery alliance with Cloud Pharmaceuticals.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Investments like these are helping to drive explosive growth in the health AI sector. Recent market research from Global Market Insights (GMI) suggests that from a fairly modest level of $760m in 2016, the market will surge in the following eight years, growing at more than 40% per annum to top $10bn in 2024.
Pharma researchers are using the technology more and more for labour-intensive tasks like target identification, drug discovery and design and compound screening. And the technology is allowing the discovery process to be turned on its head, using patient-driven biological data to uncover new targets, rather than relying on the conventional trial-and-error approach.[…]
read more – copyright by www.pmlive.com
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