Life sciences companies are likely to begin experimenting further with AI in their workflows in the coming years, but they face challenges in AI adoption due to strict regulations. Machine learning has a "black box" problem, meaning that it's in many cases impossible to know how a machine learning algorithm comes to its conclusions. An AI application that detects cancer, for example, may not be able to show an oncologist how it determined the presence of cancer in a patient's body. As a result, if the oncologist used the application to diagnose a patient, they wouldn't be able to explain to the patient what makes them sure they have cancer. This issue relegates AI applications in life sciences to experiments and pilots, and widespread adoption, although likely inevitable, may not come for a while as public opinion shifts toward accepting that its diagnoses are informed by decision-making artificial intelligence and regulations evolve to match.
It's a topic that will only become more pressing as artificial intelligence (AI) solidifies its status as a "must-have" tool for sales teams across industries. From financial services to life sciences, companies are working to not only define new compensation structures, but also overcome operational hurdles and drive internal adoption. These trends mean reps may have less direct control over their ability to achieve a sales quota. Classic sales compensation design suggests that when a rep's control over the outcome goes down, the percentage of their variable, or incentive, compensation should decrease accordingly. As pharma companies move to a lower percentage of variable pay and more team-based vs. individual incentives, we're seeing this outcome play out in real time.
This is an incredible moment in time for medicine. We've reached a tipping point in the convergence of biomedical and digital innovation. More data was created in the last two years than the previous 5,000 years of human history. Computing power has expanded, and data architecture and quality has reached a place where we can extract meaningful insights to impact human health. Researchers and doctors have access to increasingly sophisticated forms of artificial intelligence and machine learning that have augmented their ability to decode disease.
Machine Learning in Pharmaceutical Market Research Report has been studied and presents an actionable idea to key contributors working in it. A thorough study of the competitive landscape of the global Machine Learning in Pharmaceutical Market has been given, presenting insights into the company profiles, financial status, recent developments, mergers and acquisitions, and the SWOT analysis. This report has published stating that the Global Machine Learning in Pharmaceutical Market is anticipated to expand significantly at Million US$ in 2019 and is projected to reach Million US$ by 2026, at a CAGR of during the forecast period. The global Machine Learning in Pharmaceutical market can be segmented based on product type, application, end-user, and region. This report gives an in depth and broad understanding of market with accurate data covering all key features of the prevailing market, this report offers prevailing data of leading companies.
Analytics India Magazine brings you the annual list of the top data scientists in India, for the fourth year in a row. We have been identifying the ingenious minds in the world of data science and analytics who are also driving the innovations across various industries in India. From building analytics team to bringing newer processes in the working, the data scientists listed here have been instrumental in changing the face of the organisation. We have considered data scientists working with an organisation or independently, irrespective of size and nature of work. Here's the list of top 10 data scientists, to draw some inspiration and motivation from.