The digital health space refers to the integration of technology and health care services to improve the overall quality of health care delivery. It encompasses a wide range of innovative and emerging technologies such as wearables, telehealth, artificial intelligence, mobile health, and electronic health records (EHRs). The digital health space offers numerous benefits such as improved patient outcomes, increased access to health care, reduced costs, and improved communication and collaboration between patients and health care providers. For example, patients can now monitor their vital signs such as blood pressure and glucose levels from home using wearable devices and share the data with their doctors in real-time. Telehealth technology allows patients to consult with their health care providers remotely without having to travel to the hospital, making health care more accessible, particularly in remote or rural areas. Artificial intelligence can be used to analyze vast amounts of patient data to identify patterns, predict outcomes, and provide personalized treatment recommendations. Overall, the digital health space is rapidly evolving, and the integration of technology in health

Friday, December 2, 2022

AI Implementation is a Long Development Project

No one is doing A.I. just because. 

More than two decades ago many people bought a computer just because. Most people did not know the difference between software and hardware.   They discovered the computer could not even turn on without software.

I would explain to people that the hardware was like their television, displaying a snowy or blank screen until an antenna was attached and a channel was selected to watch a program.   The program, of course, was the software.  There the similarity ended since the programs were analog.

Artificial intelligence is much more than programming. The heart and soul of A.I. is machine learning. A.I. is not like conventional software.  A.I. is made out of the creative application of algorithms, experimentation, and highly recursive processes.

It is identical to the Musk method, tries, fail make changes try again.

Organizations have been developing many machine learning models, but one study showed that only 47% made it into production.  It is not an application that can be taken off the shelf and distributed to potential clients.  The average time was 8.6 months to go from prototype to production.  Add to that time is the time to develop a prototype.  It takes many iterations of trial and fail, over and over. 


 

Putting your AI Project into the Fast Lane

Four Things you need to know before Starting a new AI  project

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