Artificial Intelligence (AI) is the science and engineering of making intelligent machines, that have the computation ability to engage on behaviours that humans consider intelligent. It develops Expert Systems that runs conversational software – technology that enables machines to converse with humans in natural language. AI has progressed a long way. It has started influencing decisions. It has begun to facilitate data delivery, analyze data trends, forecast, develop data consistency, quantify uncertainty and suggest mitigating steps, anticipate users’ data needs, provide them with the necessary information in the most appropriate forms, and even suggest alternate courses of action.
Artificial Intelligence through Expert and knowledge-based systems, is being used within the clinical environment. Expert Systems contain medical knowledge and data repository, especially for a very specific task, and are able to compare and reason with this data collected from individual patients to come up with justified conclusions. The knowledge base within the expert system is derived from a set of rules.
As Enrico Coiera had laid out in his Guide to Medical Informatics, the Internet and Telemedicine, there are different clinical/healthcare tasks to which AI and expert systems can be applied. Some of them are:
- Generating alerts and reminders. An expert system attached to a monitor can warn of changes in a patient’s condition. It might even scan laboratory test results or drug orders and send reminders or warnings through an e-mail system.
- Diagnostic assistance. An expert system can help come up with likely diagnoses based on patient data.
- Therapy critiquing and planning. Systems can either look for inconsistencies, errors and omissions in an existing treatment plan, or can be used to formulate a treatment based on a patient’s specific condition and accepted treatment guidelines.
- Agents for information retrieval. Software ‘agents’ can be sent to search for and retrieve information, for example on the Internet, that is considered relevant to a particular problem. The agent contains knowledge about its user’s preferences and needs, and may also need to have the medical knowledge to be able to assess the importance and utility of what it finds.
- Image recognition and interpretation. Many medical images can now be automatically interpreted, from plane X-rays through to more complex images like angiograms, CT and MRI scans. This is of value in mass-screenings, for example, when the system can flag potentially abnormal images for detailed human attention.
The use of AI is growing by leaps and bounds, especially with the available data in the more current EMR/EHR Systems. Stay tuned for some really exciting usage of AI….
Featured Image: Spandan
Source by Dipak Chatterjee