A team of researchers at the University of Nottingham has revealed that a tool powered by artificial intelligence (AI) could predict the death date of humans.
During the study, the AI tool analyzed the data of 5,00,000 people and it successfully guessed who will perish soon. As per the researchers, this model showed better results than the models developed by doctors.
The participants in the study were aged 40-69 and the data was collected from the UK BioBank between 2006 and 2016.
The AI tool used a sophisticated algorithm for the prediction and it made use of various input factors like lifestyle differences as well as dietary habits of participants included in the study.
"We have taken a major step forward in this field by developing a unique and holistic approach to predicting a person's risk of premature death by machine-learning. This uses computers to build new risk prediction models that take into account a wide range of demographic, biometric, clinical and lifestyle factors for each individual assessed, even their dietary consumption of fruit, vegetables and meat per day," said Dr Stephen Weng, a University of Nottingham researcher and the lead author of the study in a recent news release.
The researcher also added that machine learning models are more effective in predicting mortality when compared to traditional standard models.
"We mapped the resulting predictions to mortality data from the cohort, using Office of National Statistics death records, the UK cancer registry and 'hospital episodes' statistics. We found machine-learned algorithms were significantly more accurate in predicting death than the standard prediction models developed by a human expert," added Weng.
Even though the advent of AI is racking up criticisms from many corners, the future of medical science is expected to be ruled by AI-powered tools.
In the medical sector, the introduction of artificial intelligence has provided mankind with an incredible opportunity to leverage incredible computational power for multifarious tasks including cancer detection.