Artificial intelligence (AI) offers new ways to solve complicated problems and new possibilities for the future of transformation in the health care sector.
Artificial intelligence makes it possible for machines to learn from experience, adjust to new input and perform humanlike tasks. Most AI examples that we hear about today, from chess-playing computers to self-driving cars, rely heavily on deep learning and natural language processing. Using these technologies, computers are trained to accomplish specific tasks by processing large amounts of data and recognising patterns.
Related: How Facebook Destroyed Digital Banking
AI in health care uses algorithms and software to approximate human cognition in the analysis of complex medical data. The primary aim is to analyse relationships between prevention or treatment techniques and patient outcomes.
Why do you need an analytics platform
Despite many investments in both innovative and legacy AI environments, most AI projects outside of the high-tech industry are stuck in the prototype phase. Rarely do they deliver the expected business value. The primary challenge is to operationalise AI applications and embed them into enterprise business processes. You need an analytics platform to succeed.
An analytics platform is a software foundation engineered to generate insights from your data in any computing environment. Built on a strategy of using analytical insights to drive business actions, this platform supports every phase of the analytics life cycle – from data, to discovery, to deployment.
With an analytics platform you can develop managed, governed AI applications that are scalable and have an integrated security model. In addition, these applications can be evolved using the support of an end-to-end analytics life cycle.
SAS customers who use the SAS® Platform to develop and deploy AI applications achieve crucial benefits:
- Availability of AI techniques such as deep learning and natural language processing.
- The ability to move AI applications from prototype, development and test all the way to production. In short, support for industrialisation of AI applications within the enterprise.
- Participation in new innovative ecosystems by leveraging open source through SAS APIs to augment and add capabilities such as data transformation, distributed machine learning and automated deployment.
How can artificial intelligence help health care
By using AI in an advanced analytic predictive model based on new data and historical behaviour, health care professionals receive diagnostic suggestions based on patterns from both statistical and real-time data.
Many organisations right now are devoting copious resources to develop algorithms based on AI. As I see it, we will see the real value from AI when we succeed in having the algorithms brought into a production environment. This means that the algorithm will bring value to one or many hospitals, and not only to the person who developed the algorithm. That is where many health care organisations are struggling, because they do not have the correct platform for this process.