10 Uses for artificial intelligence in pharma beyond drug discovery
Artificial intelligence (AI) in pharma as the use of automated algorithms to perform tasks which traditionally rely on human intelligence to develop new drugs.
But it goes beyond drug discovery.
There are plenty of other uses for AI in pharma and this article will show 10 of them.
1. Remotely Monitor Patients
AI has been used to remotely monitor patients with Parkinson’s disease. The technology uses a mobile app to monitor how the patient uses their hands. Through a smartphone camera, the movement is then recorded and sent for analysis. The AI judges the severity of the disease.
This technology was created from a joint partnership between Tencent Holdings and Medopad, allowing healthcare professionals to monitor their patients and set drug doses based on the results sent back from the app.
The AI can also tell medical professionals if the dose needs to be changed.
2. Generate Data and Models
While this can also be used for drug discovery, generating data and models can also be used in pharma for predicting how drugs will affect a patient.
For example, a startup company, namely TARA Biosystems, are using AI to generate models from large, high-quality datasets. In particular, this company focuses on mature cardiac tissue engineered from induced-pluripotent stem cells.
This AI will allow scientists and researchers to predict the effects a drug may have for heart conditions.
3. Repurpose Existing Drugs
As Healthcare Weekly reports, AI will certainly change every aspect of our lives, with one way by repurposing existing drugs.
For example, Biovista is using AI in pharma to analyze data and find similarities between a disease and treatment compounds. This technique is being used to reposition late preclinical stage drugs across a variety of diseases and accelerate treatment opportunities.
It doesn’t matter if the drugs are shelved, in the process being manufactured or currently on the market for sale.
4. Drug Adherence And Dosage
This next use for artificial intelligence in pharma relates to proving how successful a drug as part of the clinical studies process. After all, it’s not difficult for patients to take the incorrect dosage of a drug or not even take it at all.
Patients that don’t follow the rules can skew the results or even be removed from the process.
This is where AI and machine learning in pharma can help. It has the potential to decide whether to initiate or withhold the required dose during each interval. This will ensure reliable clinical studies results, as well as helping in real-world situations.
5. Design Clinical Trials
There are quite a number of AI applications that are being used to design clinical trials.
For example, GNS Healthcare is using AI to transform diverse streams of biomedical and healthcare data into computer models representative of individual patients. This gives scientists and researchers the ability to provide a more personalized healthcare products by revealing the best options based on that individual.
Another company in this space is PathAI, who is using AI to improve pathology analysis, enabling researchers to identify patients that would benefit from novel therapies.
6. Image Analysis
AI can help in this scenario by improving how doctors and healthcare professionals can analyse images such as x-rays and MRI scans.
Researchers at Stanford University are using AI that can perform better than human radiologists. They used the AI on 14 diseases, of which AI outperformed the human doctor for 1 disease.
This number can only increase and we can expect AI and other technology to improve the pharma and healthcare industries.
7. AI Bots
Virtual assistant bots can be used to learn patient history and make intelligent recommendations for treatments and more. They can also be used by healthcare professionals themselves for a variety of tasks, including administrative and medical.
The aim is to improve the patient experience. Because they can be personalized, patients can better interact with the bot, engage better with the doctor and perform jobs such as scheduling appointments.
Plus, because they are extremely clever, they can be programmed to perform tasks based on the environment and what’s needed at that time.
8. Help Patients With Automated Cancer Diagnostics
AI can be used in pharma to help patients find the correct medicine. This has been applied in the real-world by AstraZeneca and Ali Health to develop AI technology by improving ambulance pickups and smart cancer diagnostics in China.
This allows patients to receive better diagnoses while on the way to a hospital where they can get the care they need, potentially saving life-saving time.
These companies are hoping that as the AI develops, patients can get drugs quicker and for much cheaper than current solutions.
9. Match The Right Patients With The Right Drug Trials
An application of this has been produced by one of the most famous AI tools developed – IBM Watson. Clinicians can find a reliable list of clinical trials for eligible patients compared to standard methods.
Watson analyzes all aspects of a patient’s health record before recommending them for a drug trial. Along with recommending drug trials, it will also exclude patients for if they are classed as unsuitable or irrelevant.
Everything is done in real-time so practitioners can see the results before deciding if that patient should be considered.
10. Screen Children For Autism
This final use for artificial intelligence in pharma beyond drug discovery comes from tech giant Apple. They have created the ResearchKit, giving researchers and developers the ability to create healthcare apps for medical research. This tool works with HealthKit, allowing professionals to access data such as calories burnt and steps taken by the user.
The data collected by these apps can then be used to eliminate control groups for clinical trials and reduce recruitment bottlenecks.
So as you can see, AI can be used in pharma for more than just drug discovery. While these are just 10 other applications, there are far more being developed, tried and tested as we speak. There is so much to come from AI in pharma and it’s just a matter of when, and not if, they come into play.
This article is contributed by Julian Gnantenco @ JGBilling