AI in Life Sciences – How It Is Booming in 2022
The healthcare, pharmaceutical, and life sciences industries are riding high on technological terms. It dramatically improved our outlook on life. Artificial intelligence is becoming the surfboard that keeps us afloat. They are the science of developing computer programs and technology that do complicated tasks while assuming a human-like level of intellect. And in life sciences, its usage is rising day by day.
AI is becoming increasingly important in our daily lives. It has recently become the focus of attention in various industry sectors, ranging from industrial to life sciences. In the coming years, life sciences organizations are anticipated to begin experimenting with AI in their processes, making their tasks more straightforward. If you are still unaware of the bombardment of AI in Life Sciences, keep reading. Have a sneak peek of stats, case studies, and applications depicting the boom of AI in the life Sciences sector.
AI Is Becoming Need of Hour in Life Sciences Segment
According to Acumen Research and Consulting, the global industry will reach $8 billion by 2026. There is a significant overlap between AI and extensive data capabilities—where information processing is optimized to help address commercial and real-world challenges. AI has various potential benefits for both consumers and businesses in Life Sciences, including:
- Using chatbots to enable patient self-service
- Using computer-aided design to diagnose patients more quickly
- Image data analysis in drug research to evaluate the molecular structure, and by radiologists to analyze and diagnose patients.
- Using more insightful clinical data to personalize therapies
Current Market of AI Stating its Growth in Life Sciences
- As per the report by Grand View Research, the global Artificial Intelligence in healthcare market size was valued at USD 10.4 billion in 2021 and is expected to expand at a compound annual growth rate (CAGR) of 38.4% from 2022 to 2030.
- As per an NCBI study in 2020, AI-based algorithms accurately detected 68% of COVID-19 positive cases in a dataset of twenty-five patients diagnosed as negative cases by care professionals.
- Over the next two years, 94 percent of pharma experts expect AI technology to impact the pharmaceutical sector significantly.
- According to AstraZeneca’s Global Head of Enterprise, AI will be “the major drug discovery tool by 2027.”
Case Studies Showing Empowerment of AI in Life Sciences
i). AI Improves Predictions to Combat Serious Illness
AI in Life Sciences adds significant value by increasing the speed with which scientists and healthcare practitioners can process and use data. As recently noted in Wired Magazine, Amgen, the world’s largest independent biotech company, is one company at the vanguard of progress in the sector. The company can battle serious ailments such as cancer and cardiovascular disease by integrating life sciences and big data. Among the numerous examples given are:
- Increasing the accuracy of osteoporosis risk forecasts in women, lowering the risk window from ten to two years.
- Developing machine learning algorithms and devices to forecast cardiovascular disease risk before it occurs.
- Providing AI-driven insights about patient responses to various treatments to clinicians
ii). Monitoring of Patient Mobility
The healthcare team is bustling. Consider intensive care unit (ICU) nurses, who frequently have numerous patients in severe conditions under their care. During long-term therapies, patients’ total recovery can be hampered by limited mobility and cognition.
It is critical to keep track of their activities. Stanford University and Intermountain LDS Hospital researchers deployed depth sensors integrated with ML algorithms in patients’ rooms to track their mobility to enhance results. 87 percent of the time, the technology correctly identified motions. Eventually, the researchers hope to send alerts to ICU staff when patients are distressed.
iii). Drug Development Clinical Trials
Conducting successful clinical trials is one of the most challenging difficulties in medication development. According to a paper published in Trends in Pharmacological Sciences, it can take up to 15 years to bring a new – and potentially life-saving – medicine to market. It might also cost between $1.5 billion and $2 billion.
Approximately half of that time is spent in clinical studies, most of which fail. On the other hand, researchers can use AI in Life Sciences to identify suitable patients to participate in the tests. They can also monitor their medical reactions more efficiently and correctly, saving time and money.
iv). Quality Electronic Health Records (EHR)
Any healthcare professional will tell you that complicated EHR systems are the bane of their existence. Historically, clinicians would hand record or type observations and patient information, and no two did it the same way. They frequently did it after the patient visit, which invites human error.
Interactions with patients, clinical diagnoses, and prospective therapies, on the other hand, can be augmented and documented more correctly and in near real-time with AI- and deep learning-powered voice recognition technology.
AI Applications in Life Sciences
a). Manufacturing Customized Medicine
Regarding pharmaceutical dose, we are currently following the ‘one size fits all approach. When treatment is designed, or the assay is set, little knowledge about the victim is taken into account. The game-changer, AI programs, may access digitalized patient health accounts and recommend the best treatment approach.
b). Research Development and Manufacturing
Drug development is a time-consuming, labor-intensive, and costly process involving selecting from many potential compounds. Compared to human efforts, AI in Life Sciences can browse and cross-reference massive and multiple datasets more quickly and precisely. This results in a complete list of potential therapeutic candidates in a shorter period.
c). Innovation of New Drugs
It takes more than a decade and billions of dollars to bring a new drug to market. AI benefits from combining knowledge from many sources (hospitals and experimentation laboratories) in a cooperative manner. Aside from that, AI aids in the development of more dependable healthcare interfaces and protocols, hastening their market entry at a reasonable cost.
d). Diagnosis of Diseases
Inaccurate prognosis and infection diagnosis can result from incomplete medical records and many patients. Buoy Health is an AI-built chatbot that attends to the patient’s health issues and accompanying indicators and then uses these algorithms to guide the patient to appropriate therapy. AI programs that scan medical images, such as those generated during radiation and mammograms and identify the condition have already been discovered.
For example, an AI program that detects cancer may be unable to demonstrate to an oncologist how it determined the existence of cancer in a patient’s body. As a result, if an oncologist utilized the program to diagnose a patient, the oncologist would be able to explain to the patient why they are confident they have cancer.
e). Aids in Effective Radiology
Current diagnostic procedures rely on invasive techniques or extracting information from radiologic pictures. These include data from X-rays, CT scans, and MRI devices. By incorporating virtual biopsies, AI-based radiology technologies will help clinicians gain a more precise and complete understanding of how a disease progresses.
f). Expanding Healthcare Access
The absence or scarcity of skilled professionals such as radiologists or sonography technologists can severely limit access to life-saving care. This is most common in the world’s emerging and developing regions.
The AI-powered technology that empowers victims to address and prevent specific health issues has gained appeal in such locations. Individually, healthcare providers can perform eleven symptomatic tests and upload data for consultation.
Top Players Leading the Global Market of AI in Life Sciences
Below are the top players leading the market of Artificial Intelligence in LIfe Sciences worldwide:
- IBM Corporation
- Atomwise, Inc.
- Nuance Communications, Inc.
- NuMedii, Inc.
- AiCure LLC.
- APIXIO, Inc.
- Insilico Medicine, Inc.
- Enlitic, Inc.
- Sensely, Inc.
- Zebra Medical Vision
FAQs – Frequently Asked Questions
1). What is the current market size of artificial intelligence (AI) in the biological sciences?
The global artificial intelligence (AI) in the life sciences industry was worth $1.3 billion in 2021 and is expected to be worth $6.7 billion by 2030.
2). What factors cause the boom of AI in Life Sciences?
Various reasons such as growing costs, the desire to improve productivity, disruptions created by different novel technologies, the rapid expansion of data-driven AI, the increasing requirement for robot autonomy, and advances in deep learning are considerably driving the global AI in Life Sciences market.
3). Which region will be the global leader in artificial intelligence (AI) in life sciences?
As per the Precedence Research over the forecast period, the North America region will lead the global AI in Life Sciences market. The United States is the worldwide leader in the AI in life sciences market. This increase can be due to increased demand for AI technology across all life sciences applications.
4). What is the expected growth of artificial intelligence in the years?
From 2022 to 2030, the global artificial intelligence in the healthcare market is predicted to develop at a compound yearly growth rate of 38.4 percent, reaching USD 208.2 billion.
5). How much revenue can be expected to be generated with AI adoption?
It is recorded by Grand View Research report that the expected revenue forecast in 2030 will be about USD 208.2 billion
Every business looks into how AI might improve quality and save costs, and the life sciences are no exception.
Opportunities are being pursued throughout the industry in light of widespread governmental efforts to rein in the ever-increasing cost of healthcare, the use of AI to reduce the cost of drug discovery and delivery of healthcare services, and improve the efficacy of product development and speed to market.
The ability of AI to analyze large datasets can make a significant difference in life sciences research.
However, AI’s participation in drug discovery and others is not without challenges. Therefore, with evolution, we can witness more and more innovations in terms of AI in Life Sciences.