‘BIG 10’ Ways Artificial Intelligence Is Transforming Life Sciences
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The invasion of scientific breakthroughs and new technologies is relentless and continues to throw challenges at us. R&D data analysis can redefine our understanding of life science. Artificial intelligence has stepped in to help us navigate this vast ocean of data!
Healthcare, life sciences, and pharmaceutical companies are no strangers to riding high on technological waves. One such wave that swept the world in the 1990s was the Human Genome Project. It fundamentally altered our outlook on life. Aside from basic sequence information, underlying mutations and gene interaction patterns further expand the data repository. Researchers working on the Human Microbiome Project discovered over 100 trillion microbes with which we interact and may have a negative or positive impact on our health.
Artificial intelligence (AI) is the surfboard that will keep us afloat in these turbulent times. In broad terms, it is the science of creating computer programs and technologies that perform complex tasks while simulating human-like intelligence levels. By implementing sophisticated AI tools, it is possible to understand the enormous amount of unstructured data consisting of images, texts, and sounds faster and more efficiently and boost life science business consulting services.
Let’s take a look at the “Big 10” impactful AI applications in life sciences:
Producing Personalized Medicine | Life Science Business Consulting
Regarding medicine dosing, we currently follow the ‘one size fits all theory. When developing a therapy or determining the dosage, little information about the patient is taken into account. Life science business consulting companies have the potential to access digitized patient health records and recommend the best treatment plan using AI.
Also, by continuously monitoring several parameters, AI may allow doctors to revise the therapy and introduce a more effective alternative if the disease mutates or adjust the dose size. Enlitic develops deep learning tools for analyzing unstructured medical records (images, medical history, blood tests, EKGs, genome reports), allowing doctors to better meet their patient’s needs in real-time.
Drug Manufacture and Discovery
Drug development is a time-consuming, labor-intensive, and costly process involving screening many potential molecules. When compared to human efforts, life science business consulting firms use AI-based programs to scan and cross-reference large and complex datasets more precisely and quickly. This yields a more accurate list of potential drug candidates in less time.
Introducing Drugs and Therapies to the Market
It takes billions of dollars and more than a decade to bring a new drug to market. AI used by life science business consulting firms helps put all the data obtained from various sources (hospitals and research labs) into a compatible format. Aside from that, AI helps develop better healthcare networks and protocols, hastening their introduction into the market at a reasonable cost.
Designing Clinical Trials using AI
Artificial intelligence is becoming increasingly important in clinical trial design, estimating the optimal sample size, and implementing them remotely on participants spread across a larger geographical area. This lowers the cost and increases the likelihood of receiving relevant and accurate data.
Many cases and incomplete medical records can result in inaccurate disease prediction and diagnosis. However, AI platforms that life science business consulting firms use can scan medical images, such as those generated during mammography and radiotherapy, and identify diseases already in place. For instance, Buoy Health, an AI-based chatbot, listens to a patient’s health issues and associated symptoms before guiding them to appropriate treatment.
Introducing Robotic Surgery
The field of robotic surgery is gaining popularity. Surgeries can now be performed in previously inaccessible locations using the da Vinci robot. A trained robot will be capable of performing each operation consistently and accurately. The accuracy and consistency of the surgery will be unaffected by its duration. It is said to be superior to human performance, which will inevitably deteriorate over time.
Logistics and Supply Chain Management
AI can also help drug manufacturers and pharmaceutical companies transform their businesses. For example, AI used by life science management consulting firms makes it easier to forecast demand and then scale production based on demand.
Applications in Scientific Publishing
Artificial intelligence technologies that life science business consulting firms use are fundamentally altering publishing protocols. It helps address important issues such as finding new peer reviewers, combating plagiarism, and detecting data fabrication. This will not only help to accelerate scientific communication and reduce human bias, but it will also help to maintain publishing quality.
Developing the Next-gen Radiology Tools
Current diagnostic methods rely on either invasive techniques or extracting information from radiological images. Examples of this are data from CT scans, X-rays, and MRI machines. By performing virtual biopsies, AI-based radiology tools will allow clinicians to gain a more precise and detailed understanding of how a disease progresses.
Increasing Access to Healthcare in Developing Regions | Life Science Business Consulting
A lack of trained professionals, such as radiologists or ultrasound technicians, can severely limit access to life-saving care. This is common in emerging and developing countries around the world. In such areas, the AI-powered tool ‘Telemedicine,’ which enables patients to address and prevent certain health issues, has grown in popularity. The healthcare startup ‘WeDoctor’ can run eleven diagnostic tests on its own and upload data for consultation in an automated fashion.
Artificial Intelligence Application Challenges
Because of its heterogeneity and unstructured format, a large amount of data is clearly untapped. Although AI used by life science business consulting companies seems to be a promising approach for advancing life science, some challenges remain.
According to a recent Pistoia Alliance survey, the main barriers to AI implementation are a lack of skills (44 percent) and access to data (52%).
Many stakeholders are still unaware of the depth of the compiled data. Thus, there’s an urgent need for education. Highly skilled and trained data experts are required to meet this challenge.
Also, feeding hygienic data to AI platforms is a prerequisite for receiving high-quality data. Only data that is consistent, clean, and follows the FAIR principles produces reliable results.
To fully realize the potential of artificial intelligence, it is necessary to create a concrete framework and start incorporating ethics to ensure data privacy and fairness. It should also start with conceptualizing the algorithms that power artificial intelligence.