HOW DOES AI WORK?
Artificial Intelligence Software Development Strategy
AI software development in healthcare and medicine uses a variety of different methods and techniques to achieve desired outcomes. To achieve success, artificial intelligence development companies must ensure that they have a sound strategy in place that will address the AI needs for that particular project. AI in particular benefits from long-term observation and refinement to produce the best results. It is critical for the success of AI applications that a long-term strategy is put into place to ensure that the most is made of the potential of the artificial intelligence solutions being developed.
Artificial Intelligence Software Development Strategy
- Categorize the data set.
- Determine the optimal AI format for processing information from data set.
- Structure the database to capture all relevant data and conform to AI model.
- Program AI machine learning algorithms.
- Feed initial test data and refine algorithms based on observed machine learning.
- Launch AI application to gather real information.
- Refine algorithms based on observed machine learning to improve AI outcomes.
1. Categorize the data set.
The first step is to categorize the data set. Will the AI medical app be handling visual data, quantifiable data, or textual data? In some datasets, each particular entry may be a collection of many variables. An example may be a patient record, which combines demographic information with basic health statistics, health and treatment histories, and more. How many variables and how complex are their relationships within each given input entry? How complex are the relationships between each input entry? These questions will help determine how datasets are to be structured in the database, and which artificial intelligence methods will produce the best information from AI processing.
2. Determine the optimal AI format for processing information from the data set.
Once the data set has been categorized, attention can be turned towards the optimal AI format. Visual information will want to deploy one of the many models of computer vision. Simple computer vision apps can be created with symbolic learning, but most computer vision applications will use an artificial neural network. Apps which require heavy natural language processing will also demand an artificial neural network, likely a recurrent neural network (RNN) with long short-term memory to properly interpret semantics within context.
The type of AI information processing will matter, too. While probability outputs can be given with some of the simpler AI technologies, artificial neural networks are practically a must for simulating any kind of judgments or classifications predicated on probabilistic analysis. This is especially true if there is a deep learning need for automated continuous improvement in output.
3. Structure the database to capture all relevant data and conform to the AI model.
Once the data sets used in the artificial intelligence healthcare application are determined and categorized, the database can be created based upon a structure that maximizes the information processing potential of the medical AI app. Particular attention should be paid to the hierarchy of information. For example, in electronic health records, variables can be grouped into subsets within each entry. Some artificial intelligence information processing will concern only particular subsets, such as the treatment history to date (or perhaps even most recent treatment history). Grouping variables into various sets and subsets in nested information hierarchies will greatly aid in the development of AI machine learning algorithms to achieve particular outcomes.
4. Program AI machine learning algorithms.
Once the database has been structured and developed, the actual artificial intelligence machine learning algorithms can be programmed. Here, the objectives need to be broken down into smaller procedural sets that reproduce the complex deductive reasoning of human intelligence. In the example of an AI application processing EHR data, some more weight may be given to the amount and severity of recent treatments versus treatments of the distant past when determining healthcare outcomes. Demographic information may be weighed differently depending upon the different effects those factors have on health outcomes. Additionally, some programming for unsupervised learning may be warranted to find more subtle patterns underlying health outcomes that otherwise would not be noted. Whether it is desired for the AI machine learning algorithm to automatically adapt its judgments based on such patterns, or whether it is preferred that this is simply identified to give developers a chance to consider those factors in its long-term development of the AI application, should be determined at this stage.
5. Feed initial test data and refine algorithms based on observed machine learning.
A soft alpha launch of the AI healthcare application should be commenced at this stage, with an initial feed of data to observe real-world functionality. Debugging based on the discrepancy of observed behavior versus desired behavior will commence.
6. Launch AI application to gather real information.
After the initial alpha launch and internal testing, the AI healthcare application will go through a beta launch so that AI functionality can be observed with real-world data. The more data accumulates and the more functionality is tested, the easier it will be to refine the artificial intelligence programming to ensure optimal results.
7. Refine algorithms based on observed machine learning to improve AI outcomes.
The last stage of the artificial intelligence software application development strategy is to facilitate continuous refinement of the AI machine learning algorithms based on actual observed results to improve functionality over time. In supervised learning, results will be classified according to their degree of success by human observers to help refine the heuristics of the machine learning algorithms, or the relative weight of particular input and output stages of the neural network layers. Unsupervised learning strategies can also benefit from refinement when the algorithm itself is programmed with reinforcement learning that allows the machine learning to automatically adjust its information processing structures based upon real-world results. In either case, it is the refinement stage of AI development that allows artificial intelligence to really shine. Expanding knowledge, learning from experience, and improved judgment through time are all unique capabilities that AI deep learning models offer. It is these qualities that demonstrate the human-like intelligence that classifies the greatest potential of artificial intelligence in healthcare.
The potential for AI applications in healthcare is endless. ArtificialIntelligence.health is an AI software development company that is ready to show medical organizations how they can speed workflows, improve efficiencies, automate intellectual work, and produce richer information with artificial intelligence healthcare applications. Find out how AI can positively impact your medical organization. Contact ArtificialIntelligence.health today.