How is AI revolutionizing life sciences?
Life sciences have surfed technological waves for a long time, one of the hits being The Human Genome Project in the 1990s.
However, even as new technologies and scientific breakthroughs continue to positively impact life sciences, challenges such as analyzing large amounts of data are arising.
Artificial intelligence has, however, stepped in to assist scientists and researchers navigate the large amounts of data and make better use of it to improve lives in various endeavors.
In fact, according to a Research and Markets report, the market size of artificial intelligence in life sciences was valued at $1,255.3 million in 2020 and is projected to be worth $3943.96 million by 2025.
Broadly speaking, artificial intelligence is the science of developing computer programs and technologies like deep learning, natural language processing, and machine learning algorithms to perform complex tasks without direct human input.
In this article, we’ll discuss 4 ways artificial intelligence is transforming life sciences.
Let's get started.
Clinical trials are often accompanied by an enormous ocean of data.
This is because researchers have to gather small amounts of data from millions of patients, like blood samples and records of experiences with particular drugs or vaccines to look for patterns across the population and improve health outcomes.
In addition to patient-generated data, clinical trial data is also routinely collected from different unstructured data sources including health records, product and disease registries, and medical monitoring devices.
However, the traditional flow of this data across the clinical trial life cycle is complicated because it is marked by manual effort, errors that call for rework, and ultimately inefficiencies that leave researchers unable to draw actionable conclusions on time.
Artificial intelligence plays a crucial role in designing clinical trials by estimating ideal sample sizes for data collection and implementing them on patients across many locations.
By intelligently automating data management across the clinical trial life cycle through structuring, standardizing, and digitizing data elements, researchers can streamline processes like data gathering on-site, which gives them more time to focus on value-added tasks like patient engagement.
Actually, according to a recent case study, a major pharmaceutical company was able to reduce its oncology clinical trials by 3-4 years upon partnering with Cognizant AI.
In addition to that, AI leverages natural language processing that interprets clinical trial data elements to feed into downstream systems while automatically populating required analyses and reports.
Surgical operations require surgeons to work with precision while making incisions or performing other tasks like assessing internal organs.
One mistake could lead to fatal consequences like severe blood loss or further complications.
With the repetitive and lengthy tasks involved in conventional methods of carrying out open surgeries, surgeons are left fatigued and can only take care of a few patients daily.
The rise of artificial intelligence has revolutionized life sciences by introducing the use of collaborative robots in the operation room.
By using deep learning data collected from watching surgeons perform, AI is automating surgical procedures.
Together with complex machine algorithms and powerful libraries like OpenCV, you can determine patterns within surgical procedures to develop better practices and improve the accuracy of collaborative robots with sublimeter precision.
In fact, these free image processing projects on Great Learning will help you understand how AI assists surgical robots with machine vision to analyze scans, facilitate instrument positioning, and detect cancers.
What’s more, since robotic surgery is minimally invasive, patients can enjoy benefits like:
A case study by Cleveland Clinic reports on how a 62 year old patient was able to go home in two days after a minimally invasive robot-assisted surgery procedure compared to the one and a half weeks it would have taken for an open surgery.
In addition to that, the patient experienced less pain and quickly returned to normal activity.
In daily medical practice, patients need to undergo assessments by their healthcare providers before a diagnosis is made and treatment is administered.
However, diagnoses are not always correct because they mainly depend on insights from the patient, images from X-Ray scans, and the healthcare provider’s knowledge.
When it comes to complex diseases that cannot be detected fast enough like cancer which manifests itself in different ways, there is a risk of misdiagnosis.
Actually, the rate of pancreatic cancer misdiagnosis is at 31% according to a study by DDW.
Diagnostic errors are a significant patient safety challenge but they can be complex to define and difficult to detect. They include delayed, wrong, and missed diagnoses that have detrimental effects on patients, such as accelerated infections or death.
Apart from that, healthcare network systems also have to be secure from any malicious unauthorized access that may compromise patient data integrity, and this article explains how AI is implemented in email systems to guard sensitive patient data.
Given that many diagnostic errors arise due to subtle biases and missed information, there's a need for better systems to provide physicians with accurate information to mitigate these risks.
One of the ways artificial intelligence is transforming life sciences is in image analysis to interpret X-Ray images. Machine learning algorithms in this case recognize similar images from patient scans and in the process identify diseases from an early stage.
In addition to that, AI-based chatbots, through natural language processing can listen to patients as they explain their health concerns and the associated symptoms. The algorithms then guide patients to the correct therapy for appropriate diagnosis.
Developing drugs for medical purposes involves time-intensive, tedious, and costly approaches without certainty that they will succeed at curing particular diseases as intended.
Some of these approaches include gathering historical data on diseases and drugs used for treatment, and screening millions of potential molecules from natural sources like plants and fungi.
This is time-consuming and labor-intensive.
In addition to that, for drugs to be introduced into the market, they have to go through a series of tests to ensure they are fit for consumption by comparing large datasets to draw actionable insights.
AI provides an ideal solution for efficient drug discovery because it effectively stores and manages research and development data.
In fact, according to a press release by Market Study Report, the healthcare artificial intelligence segment is expected to experience a 40% growth rate focused on drug discovery and personal AI assistants by 2024.
Artificial intelligence, through cloud-based solutions, creates a single source of truth by combining data from many sources, such as lab results from patient disease and drug use.
Through machine learning and natural language processing, AI-based programs are then able to scan and cross-reference through complex datasets in a fast and more precise manner compared to human efforts.
As a result, you are able to arrive at a more accurate list of potential drug candidates within a shorter period which also doesn’t require a lot of rework due to errors.
AI is clearly the future of life sciences in areas, such as healthcare and pharmaceuticals to improve both the researchers’ and patients’ experiences.
In fact, according to a survey by Pharma IQ, 95% of pharma professionals expect intelligent technologies to positively impact drug development over the next three years.
Having gone through some of the ways artificial intelligence is transforming life sciences, such as in introducing robotic surgery, capturing patient data, and advancing patient diagnosis, I hope you can leverage some of these technologies like chat bots to capture and analyze patient data.
If you’re a pharmacist looking to advance your clinical trials, I would urge you to intelligently automate your data management cycle to drive better data analysis and draw actionable conclusions.