Is Artificial Intelligence (AI) the silver bullet that will make doctors all over the world unemployed? Will AI be able to outperform oncologist in creating treatment plans for cancer patients? Keep reading and get new perspectives on healthcare AI as I untangle opportunities and grand challenges within the field. "Too much information, too little time" is one of the big challenges in healthcare today. Patients, healthcare professionals and medical devices generate huge amounts of data.
Systems continue to evolve and expand in ways that benefit radiologists, providers, and patients. CT imaging in the emergency department (ED) is increasing rapidly. In fact, it now comprises more than 35% of all CT procedures in the United States. Today's CT scanners include technological developments that enable customers to better manage patient care, including lung cancer screening, dose guidance and regulation, spectral and multienergy imaging, and expansion of cardiac and brain imaging. These scanners and solutions also provide new levels of information to help clinicians make a more confident diagnosis at low dose, without increasing complexity in their routines.
Accelerating with an exponential growth, artificial intelligence (AI) is all set to move from experimental stages to live industry implementations and all is set to mark its presence across all industry verticals. AI is all about virtualizing human cognitive functions in the form of software brains. For organizations, harnessing AI is not optional, albeit it is critical to stay competitive. Gartner in its recent study (2018), predicts the business value derived from AI to reach $3.9 trillion by 2022. With the disruptive potential, the investments in AI are ever-increasing.
It is going to be interesting to see how society deals with artificial intelligence, but it will definitely be cool. Artificial intelligence (AI) can be defined to mean the use of intelligent machines to replicate and augment the intelligence of human beings. The Turing test was propounded to show what factors determine whether a machine operates on artificial intelligence or not. AI applications are being used in various fields such as telecommunication, banking, agriculture, manufacturing, health care, and transportation. The implementation of AI in health care aims to enhance the lives of the patients and enable physicians, doctors, hospitals, and administrators to improve health care delivery in a cost-effective and time-efficient manner. The traditional drug industry is also experiencing a wave of change due to the implementation of AI-based processes in drug discovery and development. Substitution of AI technology-based solutions in place of the traditional methods for drug discovery is expected to reduce the time for drug development. Using AI in clinical trials has reduced the time required for drug trials from 4–6 months to three months. After the analysis of the genomic data from different patients, AI helps by selecting only those patients whose genetic profile suggests it will help them to undergo testing in the clinical trial.2 Machine learning technologies, deep learning algorithms, various neural networks (such as artificial neural networks or computational neural networks), and content screening are a few examples of AI that have brought radical changes to the process of drug discovery and development.