Deep learning has great potential to reduce the turnaround time during the drug discovery process through predictive modeling. Companies are leveraging predictive analytics insights from historical healthcare data, ML, and Artificial Intelligence to design a personalized care regimen. Furthermore, companies are also supporting a diagnosis, in reinforcing large-scale population health initiatives and managing chronic diseases.
These innovations help patients in two critical ways. First, by medication adherence and second, through behavior modification techniques, which improves outcomes, making hospitals register reductions in readmissions, infections, adverse events, and mortality rates.
Advancements in IoTs, big data, and Machine Learning have facilitated data-driven and scenario-based decision-making which has encouraged all stakeholders to work together to find the solutions for the common problems. Ultimately, hospitals which deploy these technologies are bound to see the reduction in wait time, improve inventory management and safety, and minimize billed hours and the average length of stay. GE, Alphabet, and IBM are leading the space of personal.
GE Healthcare partnerships with Intel and Nvidia developed a deep learning platform to apply AI to medical imaging. GE has also collaborated with the Johns Hopkins Medicine for developing the advanced Capacity Command Center that uses analytics and simulation for better decision-making. This will help predict patient activity and strategize capacity planning according to demand.
Alphabet, the parent company of Google, is pushing Google in expanding its capabilities in medical imaging. Verily and Google developed AI software that can predict the risk of cardiovascular disease by analyzing retinal fundus images. AI software can identify risk factors, and generate attention maps which are less invasive and more cost-effective in contrast to the current tests. The collaboration has opened new frontiers in preventative care.
IBM Watson and Pfizer have also collaborated for Project BlueSky, where Watson continuously collects and monitors clinical data from patients with Parkinson’s disease. The data collected will provide a real-time estimate of a patient’s motor function, and this AI-enabled drug discovery process will speed up the clinical trial process of the companies. Phase 3 trials can be easily scaled because of AI-enabled drug discovery that can cover several hundred patients.
In a nutshell, AI has the potential to cut down health care costs by 50 percent, and additionally improve patient outcomes by more than 50 percent through cognitive automation, improving workflows in the hospital, and increasing the accuracy of diagnosis.