The global life science industry is booming. According to an independent research, the life sciences industry is among the laggard sectors when it comes to adopting AI ML applications for its various operations. In a fast changing world where AI ML tends to be overused and almost risk themselves of becoming the most provocative buzzwords for marketing and sales tactics, life sciences is a different sector altogether. It is taking things slow and steady with AI ML. The life sciences bring in $2 trillion every year and by 2030, increased dependence on Python data science and AI ML could take the global life sciences business to 50 trillion.
Industry experts believe that the run rate is possible only if the organizations begin to hire a mix of talent and experienced analysts and developers certified from leading Python data science courses. Some jobs that were unthinkable in the 2010s are now among the most popular in the AI ML world, including those that have to do with helping doctors, biologists, and drug makers. These would benefit users, patients, insurers, and overseers who would get so many far smarter options to make, enabling them to reach better decisions when it comes to finding the best service, drug, and health care facilities as per their wishes.
In this article, we point out some of the most astonishing user cases of Python data science course applications that have arrived in the research sector for life sciences recently.
Organ Transplant
Data Science is changing lives and saving millions of hopes by fast tracking the organ donation process. In the past, organ donation would take months, and some times – years to find an exact match. Even today, the chances of a successful organ transplant are less than 10%- which means a majority of organ transplant procedures are deemed to turn futile.
AI ML along with deep learning techniques are simplifying many critical organ donation processes — such as those that have to deal with bone marrow, kidney, lungs, eyes, and skin grafting. In the US of America, there are many healthcare facilities that operate AI ML based kidney donation programs to serve a larger population of acceptors, especially those living in NATO countries.
Finding New Antibiotic Combinations
Antibacterial and anti-virus combinations are costly products derived from years of extensive research and lab innovations. In the midst of the COVID-19 pandemic, the drug makers are relying on new techniques of AI ML Python data science course to find the next best antibiotic combination that can fight off SARS/ MERS and Coronavirus.
Combinations of drugs and antibiotic formulae are constantly tested and re-engineered to find the most synergistic option for certain commonly found infections such as TB, Malaria, HIV AIDS, COVID, Diabetes, Dengue, and Hepatitis. While these diseases are now mostly treatable, the potency of drugs administered during the treatment varies from patient to patient. In some cases, the risks of counter combinations from two different drugs also run high in susceptible patients, depending on their age, gender, genetic makeup, and current physical conditions.
Therefore, AI ML models are used to find the best possible drug options for patients who are at a moderate to high risk of reactions from newly launched antibiotics.
3D Imaging
3D imaging techniques highlight the new generation of healthcare services that AI ML based data science projects offer today. The most hyped application in data science has to be the one dealing with 3D imaging, CNN analyses, and GANs. Generative Adversarial Networks (GANs) in particular, spell the latest bunch of neural networking applications directly transforming the way microbes are studied for pathogen related research and drug discovery programs.
Israeli scientists have already developed a data science engine that can replace radiologists with new age cyborgs acting as pathologists. These Robo radiologists supervise and administer net best steps in radiology with 500 times higher accuracy of what humans could ever achieve in real time.
Big data science companies like Google, Microsoft, AWS, and IBM are constantly working with analysts and open source AI ML groups to harbor Biopharm and invasive technologies that not only save millions of lives but also help in identifying the next biggest global killer looming around our at-risk populations.
Like all industries have to face– life sciences too might find it hard to exactly pinpoint the most suitable ways to make AI ML play a crucial role in a personalized contactless environment.
Are you up for the challenge?
