AI is a collection of technologies brought together to cause machines to behave with human levels of intelligence – to sense, learn, comprehend, and act. From consumer experience to patient monitoring, AI innovation has revolutionized everything. It changes how people manage money, reduces drug development time through machine learning, offers algorithms for basic writing, solves complex business challenges, and much more.
AI technologies encompass the following and more:
· Intelligent products and agents
· Image, video, text, and speech analytics
· Facial, gesture, and biometrics recognition
· Collaborative robots
· Personalization, intelligent automation, and automated recommendation systems
Harnessing the power of these technologies has spurred interesting AI innovations across various industries.
Key Changes Brought by AI Across Various Industries
SCM and Logistics
AI innovation in the supply chain and logistics industries help with better decision-making and optimization of delivery processes. AI assists in warehouse management and analysis, and logistics for demand prediction, route optimization, workforce planning, and develops autonomous vehicles for shipping.
Journalism is mobilizing AI to make quick and accurate sense of complex financial reports. A case in point is Bloomberg’s Cyborg technology.
Working alongside humans, AI-powered robots are tackling assembly and stacking, quality control, and predictive analysis.
Patient recruitment, trial optimization, and drug interaction with patients could be gauged accurately with AI.
AI in med-tech promotes AI-driven medical-imaging and algorithmic analysis of clinical data. It delivers enhanced customer experience through remote patient monitoring, wearable technology, and improved surgical procedures.
AI accelerates disease diagnosis and drug development, improves clinical site selection, increases patient satisfaction, facilitates advanced medical research, and drives healthcare compliance. Thus, AI offers life-redeeming alterations through its contributions to the life science industry.
Innovations Led by AI in Life Science
Drug Development and Manufacture
Established pharma and biotechnology companies are leveraging AI-powered drug discovery to streamline their R&D efforts. This includes the calculation of substantial patient datasets into tangible information, identifying personalized medicine opportunities, and predicting potential responses to novel drugs. Precision medicine on a well-developed AI platform promises cost-cutting in drug development and also lessens the development time.
Data quality control, data transfer, and data management processes are archaic with human intervention. With AI innovation, these manual works could be eliminated through infrastructures that bridge the suppliers of data like hospitals and customers (pharma or biotech companies). Robust algorithms assist specialists in the decision-making process, image analysis, and assay analysis. Pharmaceuticals leverage AI to determine the next indication where a drug under development is likely to succeed.
Assays reveal heterogeneity in the pathophysiologic processes and factors that contribute to disease. This makes personalized medicine vital for individual patients. AI helps here, given the importance of data-intensive assays that must reveal the appropriate intervention between targets and strategies for treating patients. It promises precision medicine and modernizes healthcare in three significant areas:
· Personalized diagnosis
· Personalized treatment
· Disease prevention
Diagnosis and Disease Identification
AI innovation in disease diagnosis and identification endorses deep learning, an application point being the detection of cancer. The integration of AI and the Internet of Medical Things promises better consumer health applications. While medical IoT gathers health data, AI process the information to offer adjustments to the present lifestyles of patients. Other use cases of AI in disease detection are seen already digitized medical information. The algorithms learn thousands of existing foolproof patterns to read images. Following are a few use cases of AI:
· Detecting strokes and lung cancer through CT scans
· Assessing the risk of heart diseases and sudden cardiac death through MRI images and electrocardiograms
· Grouping skin lesions from skin images
· Detecting diabetic retinopathy in eye images
An AI model developed by researchers at King’s College London, Massachusetts General Hospital and a health science company Zoe, predicts the possibility of a person to test COVID-19-positive based on their symptoms, with 80% accuracy, through studies from the COVID Symptom Study App.
Supply Chain Transformations
Robotic Process Automation (RPA) is used to automate claims processing, and contract-management tasks in healthcare supply chains, such as validating pricing and populating the procurement systems with contract terms. When disasters disrupt supplies, medical essentials could be made available by deploying AI that helps providers anticipate stock-outs and back-orders. It would also help manufacturers collect data across complex supply chains to predict disruptions accurately and formulate remediating steps to help patients identify alternatives.
Submission Dates Optimization
Up-to-date information is indispensable for life sciences companies to ensure the safety of products. The pharmaceutical labels convey essential safety information. This leads to multiple considerations such as production run dates and artwork printing, for submitting the local product label to the respective health administration. Standard operating procedure timelines of companies govern submission due dates that are stricter than the requirement, thus augmenting cost and workload. But coalescing appending the pertinent data points with predictive analytics and machine learning could determine the optimal submission date.
AI innovation offers avenues for life sciences companies to radically transform business across organizational, processes, and people levels. AI in disease detection and drug development and management could deliver tangible information to speed the medical operational process. Greater authority and accuracy over data would enhance clinical research. Personalized medicine would be a reality with AI innovation, thus, promising better treatment for patients. Overall customer satisfaction could be achieved through RPA, machine learning, and predictive analytics.
Such growth of AI in life sciences gives masterminds leverage over novel data and technologies. Patients could nurse optimistic outlooks as new therapies and medicines could be discovered, developed