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, particularly in the life sciences.

Innovations Led by AI in Life Science

Image Source

Drug Development and Manufacture

Established pharma and biotechnology companies such as AstraZeneca, Bayer, Novo Nordisk, GSK, Pfizer, and several others 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 as discovered by Zenith Technologies using machine-learning in their manufacturing processes. In a similar vein, GNS has invested $38M into machine-learning technology to improve drug development and data analysis.

Clinical Research

With AI innovation and process automation, data quality control, data transfer, and data management processes, manual work can be eliminated. The use of cloud enterprise solutions in this regard also improve efficiency as IAG’s DYNAMIKA found. This image-based cloud computing software bridged the gap between suppliers of data like hospitals and customers (pharma or biotech companies).

Robust and precise algorithms assist specialists in the decision-making process, image analysis, and assay analysis. Image Analysis Group (IAG) compiled various case studies proving the role of AI’s advanced image and data analysis potential in various fields such as rheumatology, oncology, autoimmune diseases, etc.

Pharmaceuticals leverage AI to determine the next indication where a drug under development is likely to succeed, where a patient may be susceptible to cancer, the perfect dosage, etc. The US National Library of Medicine’s clinicaltrials.gov, for example, could be analyzed by a machine to determine the best treatment options in a big data project of collecting all the available information on the platform.

Personalized Medicine

Image Source

Assays reveal heterogeneity in the pathophysiologic processes and factors that contribute to disease, as found by several experts writing for the Journal of Experimental Medicine. 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 according to Gregg Meyer, MD, MSc, Chief Clinical Officer at Partners HealthCare. 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. This was then applied to a model of 800,000 people who showed symptoms. The app predicted that a fifth of these people could have COVID-19. As of May 2020, 3.3 million people had downloaded the app.

Image Source

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.

This could further result in a rebalancing of the healthcare supply chain through partnerships such as UPS’ Flight Forward with CVS Pharmacy for pharmaceutical deliveries by drone. Such integrations 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 since pharmaceutical labels must convey essential safety information so consumers know exactly what they are getting. 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. This is why companies like Grand View Research predict that investments in AI for healthcare will reach $31.3B by 2025, a 41.5% increase from 2018.

Conclusion

Companies like Remedy Health, Quid, Sensely, InformAI, and many others are trailblazers in incorporating AI into the healthcare industry. AI innovation offers avenues for life sciences companies to radically transform business across organizational, processes, and people levels. BioSymetrics’ Augusta, for example, can capture and analyze data released from 25 billion IoT devices as well as other biomedical data sources (EEG, MRI etc.).

Such increased authority and accuracy over data would enhance clinical research and lead to personalized medicine. AI’s abilities in disease detection and drug development and management could also speed up medical operational processes. Thus, overall customer satisfaction could be achieved through RPA, machine learning, and predictive analytics.