by Richard Nordstrom February 6, 2022
The biggest investors are the Big Tech companies-Google, Watson, AWS and Microsoft. Artificial Intelligence (AI) already found several areas in healthcare to revolutionize starting from the design of treatment plans through the assistance in repetitive jobs to medication management or drug creation. And it is only the beginning.
We have not yet reached the state of “real” AI, but it is ready to sneak into our lives without any great announcement or fanfares – narrow AI is already in our cars, in Google searches, Amazon suggestions and in many other devices. Apple’s Siri, Microsoft’s Cortana, Google’s OK Google, and Amazon’s Echo services are nifty in the way that they extract questions from speech using natural-language processing and then do a limited set of useful things, such as look for a restaurant, get driving directions, find an open slot for a meeting, or run a simple web search. But there is already more to that. A 19-year-old British programmer launched a bot which is successfully helping people to appeal their parking ticket. It is an “AI lawyer” who can sort out what to do with the received parking ticket based on a few questions. Up until June, the bot has successfully appealed between 160,000 of 250,000 parking tickets in both London and New York, giving it a 64% success rate.
According to Life Sciences 4.0: Securing value through data-driven platforms, health is being reimagined as a result of scientific and technological change and rising customer expectations. Life sciences market offerings, business models and the new capabilities needed as the disciplines of health care and technology merge to become “health technology.”
In this fluid environment, every company developing health care products and services is a data company, and therefore a technology company. Likewise, every technology company that has access to health-related, consumer-generated information or other health data is a health care organization.
At a minimum, that means redefining innovation beyond product-centric attributes tied to mechanism of action to focus on a range of outcomes linked to customer engagement, personalization and data literacy. Tomorrow’s blockbusters could well be algorithms that combine scientific, behavioral, economic and financial insights into personalized solutions designed to treat, cure or even prevent disease. Consequently, life sciences companies will need to consider how and when to participate in emerging care platforms that seamlessly collect, combine and share a variety of health data in real time. We call the platform-based business models that emerge from such efforts Life Sciences 4.0
There is an urgent need and a huge opportunity to embrace Life Sciences 4.0. The rapid and easy exchange of data has already transformed the retail and transportation industries. New entrants are using algorithms and analytics to eliminate long-standing inefficiencies and create benefits for themselves and their customers. Life Sciences 4.0, likewise, can help MedTech’s and biopharma’s unlock new value. By co-creating personalized solutions that improve health outcomes with other health stakeholders, life sciences companies can use data to preserve, or improve, their position in a rapidly changing ecosystem. From blockbuster products to data-driven platforms.
The most obvious application of artificial intelligence in healthcare is data management. Collecting it, storing it, normalizing it, tracing its lineage – it is the first step in revolutionizing the existing healthcare system.
Data is the key and putting data at the fingertips of all ecosystem customers. The most obvious application of artificial intelligence in healthcare is data management. Collecting it, storing it, normalizing it, tracing its lineage – it is the first step in revolutionizing the existing healthcare systems.
AI and machine learning are already being implemented in:
- Enhancing medical process
- Maximizing in-person and tele-health encounters
- Personalizing treatment plans
- Precision medicine and predicting response
- Medication Adherence
- Radiology repetitive activity
- Clinical trial data collection and analysis
- Drug discovery
- Keyword and topic extraction
- Speech-to-text transcription and translation
- Image classification and labeling
- Text and handwriting recognition
The need to demonstrate value has quickly been adopted and reshaped business models from blockbusters to now data driven platforms. The ultimate goals in healthcare are to improve outcomes, reduce costs, deliver global equality in care. AI will be a large part of driving success.
