Artificial Intelligence is transmuting the system and methods of the healthcare industries. Artificial Intelligence and healthcare were found together over half a century. The healthcare industries use Natural Language Processes to categorize certain data patterns. Artificial Intelligence can be used in clinical trials, to hasten the searches and validation of medical coding. This can help reduce the time to start, improve and accomplish clinical training.
"In a certain sense I think that artificial intelligence is a bad name for what it is we're doing here," says Kevin Scott, chief technology officer of Microsoft. "As soon as you utter the words'artificial intelligence' to an intelligent human being, they start making associations about their own intelligence, about what's easy and hard for them, and they superimpose those expectations onto these software systems." This might seem like a purely academic debate. Whatever we call it, surely what matters most about "AI" is the way it is already transforming what can seem like almost every industry on earth? Not to mention the potential it has to displace millions of workers in trades ranging from white to blue collar, from the back office to trucking?
DeepMind, a sister company of Google, is giving the world access to a massive protein structure database -- a gift that has the potential to revolutionize scientific research. "This will be one of the most important datasets since the mapping of the Human Genome," Ewan Birney, deputy director general of the European Molecular Biology Laboratory, which partnered with DeepMind on the database, said in a press release. Protein structure: Proteins are molecules that are hugely important to the functioning of living organisms, including humans -- practically everything we're made of and everything our cells do is determined by our proteins. "It's the most significant contribution AI has made to advancing scientific knowledge to date." Every protein is made up of a long string of hundreds or even thousands of chemical compounds called amino acids, and the way that ribbon folds on itself determines the protein's function.
Proteins are essential building blocks of living organisms. Every human cell is replete with them. While the understanding of the shapes of proteins is important for making medical advances, only a fraction of these had been deciphered until recently. The ability to use artificial intelligence (AI) to predict the structures of almost every protein made by the human body could help to accelerate the discovery of new drugs to treat disease. A program called AlphaFold can predict the structures of 350,000 proteins belonging to humans and other organisms.
Bringing the benefits of artificial intelligence into a company requires good working relationships between the data team and the business units -- and a clear focus on tangible value. Companies embarking on AI and data science initiatives in the current economy should strive for a level of economic return higher than those achieved by many companies in the early days of enterprise AI. Several surveys suggest a low level of returns thus far, in part because many AI systems were never deployed: A 2021 IBM survey, for instance, found that only 21% of 5,501 companies said they had "deployed AI across the business," while the remainder said they are exploring AI, developing proofs of concept, or using pre-built AI applications. Similarly, a VentureBeat analysis suggests that 87% of AI models are never put into production. And a 2019 MIT Sloan Management Review/Boston Consulting Group survey found that 7 out of 10 companies reported no value from their AI investments.
The continued contribution of the drug development community toward improving the quality of lives of patients, researchers, and the public at large, is and will continue to be highly dependent upon the careful execution of strategies to make vast amounts of data meaningful and usable. This is achievable by pairing data with powerful analytics and then using those insights to develop safe and effective processes and products. Although the drug development enterprise is undergoing major transformation, literature about what the sector should do to support and prepare its workforce for these changes is scant. What follows is a discussion of original research conducted by the Tufts Center for the Study of Drug Development (Tufts CSDD) to address workforce development in the era of digitization. The research is primarily based on an in-depth discussion with thought leaders and senior executives. Tufts CSDD identified recurring themes for discussion in articles in academic journals and the trade press between 2015 and 2019. Discussion topics included: (1) challenges and opportunities caused by the sector's digital transformation, (2) skills and competencies of future drug development professionals, (3) new roles that are expected to emerge within drug development, (4) changes in talent recruitment and retention practices, and (5) the reshaping of corporate mindsets and cultures to become digitally proficient organizations.
DeepMind, an artificial intelligence (AI) subsidiary of Google parent Alphabet, said it has been successful in predicting the shape of nearly every protein in the human body as well as thousands of other proteins found in 20 additional organisms that scientists rely on for their research, including yeast, fruit flies, and mice. This breakthrough is likely to assist researchers to understand human diseases better and find new drugs to treat or cure them. Some scientists have compared the DeepMind project to the international effort to map every human gene. DeepMind said in a blog post it is releasing the database for free. To set up and run the database, it has partnered with the European Molecular Biology Laboratory.
Veeva [NYSE: VEEV] is the leader in cloud-based software for the global life sciences industry. Committed to innovation, product excellence, and customer success, our customers range from the world's largest pharmaceutical companies to emerging biotechs. Veeva's software helps our customers bring medicines and therapies to patients faster. We are the first public company to become a Public Benefit Corporation. As a PBC, we are committed to making the industries we serve more productive, and we are committed to creating high-quality employment opportunities.
Quantum computing, AI and blockchain are being explored as drivers for business transformation and intelligent change by leading organizations. Quantum computing has the potential to address the computational needs of modern technological industry development in areas such as drug development and manufacturing, where traditional and supercomputers aren't able to provide the simulations necessary to further enhance and deliver new developments to these industries. Over 60 countries have developed national AI strategies and policies to promote AI development and research and explore risk mitigation using AI. Also, distributed ledger technologies using blockchain are helping to secure data and transactions in areas like finance, government, energy, and transportation. Quantum computing, AI and blockchain naturally coincide, as quantum computing will help bring new levels of computational power and efficiency as data growth and accumulation for industry solutions are on the rise.