Artificial intelligence (AI) has turned into a great tool for the pharma industry. It’s helping with drug discovery, rare disease detection, high-quality analytics, clinical trial patient enrollment, personalized patient care and more.
According to Statista, artificial intelligence revenue from the healthcare industry is expected to reach around 6.16 billion US dollars by 2022. As a result, the top 10 big pharma companies have started to invest in AI-based applications.
Besides bringing in revenue, artificial intelligence has the potential to save the pharma industry time and money. The R&D cost of a single drug can sky-rocket to a staggering 2.8 billion US dollars. So using artificial intelligence to speed up the drug discovery process has the potential to save billions.
Here are some of the applications of AI that can make a big impact on the pharma industry in 2019:
1. AI Application in Early Phase Clinical Trials
Millions of people suffer from cancer all around the world. In the US alone, 1.7 million new cases of cancer are diagnosed each year. Yet only a small percentage of patients are enrolled in life-saving clinical trials.
Multiple reasons contribute to the lack of enrollment. It’s difficult to match the right patient with the right drug trial. The process of discovery is expensive. Current rules like HIPAA that are designed to safeguard patient privacy can put in additional hurdles.
Artificial intelligence subdisciplines like machine learning and deep learning can decrease the cost of patient discovery for the clinical trials. Companies like Antidote and Deep 6 AI are using artificial intelligence to help with patient discovery. On the other hand, companies like Trial.AI and BullFrog AI are helping design clinical trials using machine learning.
We can expect to see this trend of using AI applications for clinical trials and patient discovery to continue.
2. Designing Drug Using AI and Scientific Data Modeling
The pharma industry has large sets of data both in analog and digital formats. However, analyzing this data to find relevant information is a difficult task. AI applications are helping the pharma industry model the data into formats that can be used to develop nextgen drugs.
Startups like Insitro and TARA are generating data models using artificial intelligence. Insitro uses machine learning to derive models that can help with drug discovery and development process. TARA develops predictive tissue models that speed up the development of new medicine.
Due to the usefulness of the models to the pharma industry, AI-based data modeling tools will keep playing a prominent role in 2019. Interested in leveraging artificial intelligence at your company but you don’t know where to start? Check out this list of consulting companies that work in the AI space.
3. Using AI for Drug Monitoring and Patient Care
For the pharma industry, the task of policing prescription drugs has always been a big challenge. Besides the abuse of drugs, prescription error is also a big problem.
AI-based applications can help monitor drug use and prevent both drug abuse and medication errors. For example, NIC, a government digital service provider, launched RxGov, a prescription drug monitoring program (PDMP) to fight the opioids epidemic. RxGov is powered by machine learning algorithms.
Also, AI-based chatbots have started to enter the area of patient care. The UK’s National Health Service (NHS) is using Babylon Health’s AI-powered chatbots to provide medical advice to patients.
Drug monitoring and patient care remain areas of growth for AI applications.
4. AI Enabled Image Analysis
Doctors prescribe medicines based on the diagnosis of various specialists. The accuracies of various image-based tests like X-Rays and MRI scans play a vital role in what drugs get used for particular maladies. So, naturally, the pharma industry is interested in improving diagnosis by using artificial intelligence.
Stanford University researchers have reported on an AI-based algorithm that can outperform radiologists for certain diseases. The algorithms are called CheXNeXt. After CheXNeXt was trained, it was used for 14 diseases. It performed as well as humans on 10 diseases, underperformed humans for 3 diseases and outperformed humans for 1 disease.
Naturally, we can expect to see the proliferation of these kinds of algorithms to help improve healthcare services and prescriptions.
5. AI-based Diagnostics of Rare Disorders
Due to the high diagnostic costs, rare disorders often get ignored in the medical community. But AI-based tools are making it easier to develop solutions that can help diagnose those conditions.
DeepGestalt is a deep learning application that has been trained with 17,000 images to recognize Noonan syndrome, a condition that inhibits physical and mental growth. In testing, the application was able to recognize the condition 91% of the time.
Even though in the past, the pharma industry avoided investing in rare diseases, the availability of technologies like DeepGestalt can open up new opportunities to help patients with rare diseases.
6. Improving Drug Supply Chain With AI
The pharma supply chains are complex. They involve manufacturers, distributors, pharmacies, hospitals, healthcare professionals and patients. Pharmaceutical companies have always struggled with keeping track of the drugs for product integrity and compliance.
The combination of artificial intelligence and the internet of things (IoT) devices are changing how the pharma companies monitor their products end-to-end. Now they can check whether the products are transported under the right temperature or patients are using expired drugs. AI-based applications can monitor and alert companies of any anomalies.
So, we can expect to see pharmaceutical companies investing more in using AI to manage their supply chains.
7. Pharmaceutical AI Data Model Sharing
A big area of pharmaceutical AI application development would be the improvement in data model sharing. Current open source software models are not adequate for AI applications. In traditional applications, the software can be shared without the data. But in AI, data models play a more integral role.
We can expect to see more cooperation develop across the board to help the pharma industry make faster progress. Improvements in data model sharing will help the whole industry.
What to Expect in 2019 in the Pharma Industry?
Even though artificial intelligence has been around since the 1950s, it has become more practical to use AI technologies in the last decade due to advances in the sub-disciplines of machine learning and deep learning.
As mentioned above, computer vision, another sub-discipline of AI, is also playing a role in image analysis.
Companies in the pharmaceutical industry will continue to improve on the current applications and find new ways to implement solutions. But the industry as a whole needs to cooperate more for sharing data. A great example in this direction is Accelerating Therapeutics for Opportunities in Medicine (ATOM). Hopefully, there will be more data sharing through more consortiums and organizations like ATOM in 2019.
This guest post comes from JGBilling medical billing group.