Artificial Intelligence: AI Based Medical Diagnostics
The field of AI has increased dramatically over the past 5 years. This has been due to accessibility of software components to aid developments of applications along with the accessibility of hardware that can run everything. So, what is it and how does it work?
Briefly AI utilises a weighted matrix of interconnected nodes and almost identical to a statistical decision-making matrix. AI could assess through filtering techniques, what a picture is of for example. By passing databased content through this ‘neural network’ such as pictures of socks and shoes and telling the system if it is correctly guessing each based-on image filtering techniques such as identifying shapes and colours, each node containing a decision-making event has its weighting factor changed accordingly. The process of this ‘machine learning’ is iterative and repeated multiple times until the error of guessing the answer is withing a predefined scope.
Once trained the AI can then be utilised in the field and given an unknown item to process and guess the answer. The great thing is that although machine learning takes computational power such as a server either locally or on the cloud, it normally is only needed once to optimise the node weighted decision-making values. Why is this good news, well it then allows for AI optimised applications to use it on lighter platforms. Some more intensive applications provide support for AI dedicated processors and hardware that can be added to a products design during manufacture.
AI for Medical Diagnostics and Diagnosis
Why is AI used? Well, for repetitive tasks that would be tedious to do manually and time consuming. For example, what if you need to identify a red blood cell from a white blood cell? You could do that manually, but what if you need to identify and count hundreds per minute as well as tracking their relative positions. Perhaps you could do this with static images but now what about in real time? This is where AI becomes a very useful tool.
AI can be used for the assessment of biomarkers through bioinformatics and molecular measurements providing multiple criterions for AI to work with. This currently is an exciting development in medicine presently.
Biomarkers and algorithms are used to create immunological fingerprints for the assessment of multiple disease states. Furthermore, throughput can be quicker than conventional assessment procedures, allowing clinicians to assess diseases through disease scores and managing treatment around available resources. For patients that are hospitalised or in intensive care for diseases that cause rapid changes to the patient this technology could be vital in a whole multitude of cases.
AI is already having an important part to play in assessing store customers temperatures (along with if they are stealing items from the store); the challenge is that there are many that do not show any symptoms. Currently devices that assess pre-symptomatic infections are being developed; use of ai in medical diagnosis will continue to grow.
The future of Medical Diagnosis
As you can imagine, patient management will be driven using AI assessment as well as disease severity flagging in real time. It is highly likely that such diagnostic tools will be a key tool to achieve this and integrated into one automated system of real time and fast testing techniques that are semi-automated to monitor and report changes in severity for each patient. This integration will reduce risk that patients are miss diagnosed or changes in severity are missed.
