AI is quickly scaling and being adopted by multiple industries at a rapid pace, none more than healthcare and biopharma. Not only is AI helping these groups that specialize in healthcare and biopharma with optimizing costs, but it is also opening the doors to new discoveries that can potentially rock the foundations of healthcare. With that said, let’s take a look at a few specific areas of research that is seeing AI taking the lead.
If there is one thing that AI has a proven track record with, it’s analyzing vast amounts of data from multiple sources in no time. When it comes to protein discovery and predictions, AI is helping to accelerate the discovery and prediction of proteins by enabling researchers to analyze vast amounts of data and make predictions with unprecedented accuracy. All of this is leading to breakthroughs in drug discovery and development and ultimately has the potential to improve patient outcomes.
One example of this is DeepMind’s AlphaFold which focuses on protein structure prediction. With the AI of AlphaFold, researchers are able to predict the 3D structure of proteins with much greater accuracy which in turn allows scientists to better understand protein function and how it relates to disease, which can help in the development of new treatments.
Then there is Atomwise. It comes from a biotech company that uses AI to predict the binding of small molecules to protein targets. This technology has the potential to speed up drug discovery by identifying promising drug candidates more quickly and accurately than traditional methods. Meaning, that new medicines can be discovered that are more robust at a faster rate.
Patient health records analysis
It is no secret that the bane of the medical profession is paperwork, especially paperwork related to patient health. Multiple doctors, clinics, networks, and more make putting together a complete and accurate record of a patient’s health history a costly and time-consuming process. Without the reference of these proper records, diagnosing diseases, and personalized medicine can be negatively hampered. But AI is helping to address this problem by using the power of analyzing vast amounts of data with speed and accuracy. AI algorithms can analyze large data sets containing patient health records and find patterns that can point to a specific disease or condition. Then there is personalized medicine. Other AI algorithms can use the power of predictive patterns to find how a patient will respond to a particular treatment, allowing doctors to tailor treatments to individuals, which in turn could improve outcomes.
One example of this is IBM Watson Health. This suite of AI-powered tools is designed to help healthcare professionals analyze patient health records. Watson can analyze patient data to identify potential drug interactions or suggest alternative treatments. Watson can also analyze patient data to predict the likelihood of a patient developing a particular disease, which can help doctors to take preventative measures.
Another example is Google Deepmind Health. Similar to IBM Watson Health, this AI-powered suite of tools is designed to help healthcare professionals analyze patient health records. DeepMind Health can analyze patient data to predict which patients are at risk of developing sepsis, a potentially life-threatening condition. This allows doctors to take preventative measures, such as prescribing antibiotics or monitoring patients more closely, all to reduce the risk of sepsis.
Synthetic data and creating better records
When creating these AI programs, synthetic data is key to anonymous algorithms since they mimic real-world datasets. With synthetic data, AI algorithms are able to be trained well without the risk of compromising patient privacy and violating medical privacy laws which are critical for healthcare and biopharma firms to follow. But it’s not just privacy preservation or data algorithms that bring value to synthetic data. In healthcare, it also is valuable for clinical trial simulations where algorithms can help to reduce the time and cost of conducting clinical trials and can provide valuable insights into the safety and efficacy of new drugs
Synthesis AI is a platform that allows for the creation of images and videos of digital humans and scenarios that create multi-human simulations across a varied set of environments. For example, a company can create millions of images of unique individuals to build a privacy-compliant and unbiased facial ID model for research.
Statice allows groups to easily handle their massive quantities of data by allowing for the scale of analysis and research aided by their AI which anonymizes medical and patient data to keep firms compliant with standing privacy laws.
In healthcare and biopharma industries, NLP models are being used to analyze large volumes of unstructured data such as regulations, medical literature, clinical trial data, and patient records. This is seen by NLP models analyzing medical literature and regulatory documents. This aids researchers and medical professionals to better understand regulations to maintain compliance while being able to focus greater resources on medical research.
This is done by NLP models identifying potential safety concerns or compliance issues with medical products. It does this by analyzing adverse event reports and identifying potential safety issues associated with specific medications or medical devices.
Linguamatics has a feature that allows for text mining of IDMP (IDentification of Medicinal Products). This is a set of international standards, developed by the ISO, to define the rules that uniquely identify medicinal products. IDMP is the standard for transmitting regulatory data to authorities throughout the medicine’s lifecycle from clinical trials, marketing application, approval, and pharmacovigilance.
Predicting patient outcomes
As mentioned above, AI-powered programs are excellent at analyzing large volumes of patient data and this is another area where the technology is making significant contributions. These algorithms are able to take the data they analyzed and help find patterns that healthcare providers can use to find medical conditions or treatments that are likely to be better suited for the patient’s unique situation.
But that’s not all. There are also machine learning algorithms that can also analyze large sets of data of patient data, which then in turn can be used to make accurate predictions about possible and future outcomes based on the information provided. This could assist in treating serious conditions earlier, when treatment is less costly and invasive, by finding them sooner.
ClosedLoop gives providers the ability to make accurate, explainable, and actionable predictions about individual health risks based on data fed into the program, the goal of which is to diagnose and treat issues sooner when it would be less costly.
ACTIS by Everana uses artificial intelligence and machine learning algorithms to provide predictive analytics, real-time insights, and integrated data all via the cloud. The goal of which is to lower the cost of care through predictive medicine powered by AI.
Solving problems in biology
Finally, the question of biology. A very complex subject with complex problems, AI is assisting primarily with drug discovery and development helping researchers find situations to evolving problems. One way this is happening is with AI-powered tools identifying new biomarkers and gadgets for the diagnosis of diseases and treatment. Al algorithms can also identify patterns and correlations by reviewing large data sets that can find disease progression or discover responses to treatment.
Overall, due to the large volumes of data, AI is helping researchers discover new patterns and ways to positively affect healthcare and biopharma organizations in their research so they can develop more effective treatments that will improve patient outcomes.
One example of this is NVIDIA’s own research in advanced medicine with AI. By harnessing the power of AI, the company provides computing paradigms that help in biomedical research through high-performance computing.
What do you think? Pretty great right? The future of healthcare is being shaped by AI and AI-powered tools. These are opening doors to treatment and outcomes that couldn’t have been imagined even a few years ago. But since this is a dynamic field, being on top of the latest advancements and trends is critical for any data professional. Don’t worry, ODSC East has you covered at ODSC East’s Machine Learning for Biopharma track. Meet some of the leading minds from trailblazing organizations such as Oracle, Mitre, Novartis, and more as they cover the topics impacting AI in Healthcare and Biopharma.
What are you waiting for? Check it out for yourself today while tickets are 30% off!
Some currently scheduled sessions include:
- A Natural Language Processing (NLP) Approach to Automate Patients’ Testimonials Analysis
- Unlocking the Potential of Protein Prediction in Drug Discovery
- Development Principles for Biotech Data Teams
- Synthetic Data in Healthcare: Methods, Challenges, and Use Cases
- Patient Level Prediction with Supervised Learning Models in Federated Data Networks
- Data Science @ Moderna: Accelerating Regulatory Communication with Natural Language Processing
- A Zero-shot 2D Sentiment Model Predicts Clinical Outcome in Psilocybin Therapy for Treatment-Resistant Depression
- Is Machine Learning Necessary to Solve Problems in Biology