falling from their beds . So we built a model that learns from data ,” explains Landau . “ Models are like children , they have no common sense , so we have to teach them .”
A model is , of course , only as good as its data , and Landau believes that this is how AI can save lives .
On a recent visit to one customer in Denmark , Landau got to see for himself how patient monitoring works in Danish nursing homes .
“ There is a nursing shortage there ,” Landau says . “ The AI model detects and checks when there is an issue , to save the nurses time . Being preventative is a key element to the use of AI in healthcare , by solving problems before they proliferate . Another example is in automatic stroke detection and in improving Doctor-patient outcomes . A lot of Doctors ’ time is spent making notes on paper ; if you want your doctors to save time , AI can make those notes instead , indirectly saving lives .”
How AI is saving lives To Andreas Heindl , Machine Learning Product Manager at Encord , AI isn ’ t viewed as something to replace the human , but to make them more powerful .
“ Analysing lung scans takes hours for a human , but machine learning models can perform this task in minutes , streamlining and accelerating the process ,” says Heindl . “ The result is that doctors can focus their time on more complex tasks , such as developing patient care plans , and patients can gain access to health information more efficiently .”
If there isn ' t a domain expert available , Machine Learning ( ML ) can also be employed . In many isolated locations where individuals lack access to doctors , AI may be their only option to receive an accurate diagnosis .
“ Analysing lung scans takes hours for a human , but machine learning models can perform this task in minutes ”
ANDREAS HEINDL MACHINE LEARNING PRODUCT MANAGER , ENCORD
“ AI is great at acting as a second pair of eyes , and incidental findings are a common occurrence .”
Deep learning is significantly more data hungry than traditional machine learning . No longer do you create features , says Heindl , instead , you let the AI discover the best ones .
“ Companies are realising that scaling up their annotation workflows is the issue because current state-of-the-art AI takes a large amount of training data . Data-centric AI is essential for reviewing , scheduling , and
70 March 2023