For the past 10 years, Artificial Intelligence (AI) has been rapidly spreading into most parts of our lives; for instance, in our living rooms with Amazon Alexa. However, recently our hospitals have adopted AI where it serves to analyze large amounts of patient clinical data and deliver useful actionable insights to clinicians involved in diagnostics.
But further than its ability to make ‘Big Data’ more intelligible, AI is also going through the unexpected and certainly paradoxical process of re-humanizing medicine.
How is this happening?
In the field of medical genomics, for instance, a major bottleneck for clinicians is to first sort and then find relevant insights from the enormous amount of data to help them formulate a diagnosis. This implies that clinicians spend much time analyzing the data using their own methods, some even engaging in engineering new software solutions to do so, sometimes without the knowledge, technical or financial support.
This has one important consequence: clinicians spend more time in front of their computer than with their patients. Additionally, many do not yet employ such technology, meaning patients are missing out.
The advent of AI means such technical, back office work can be fully and easily automated, with an immediate positive consequence: giving back precious time so clinicians can spend it with their patients. As AI rapidly digests an enormous amount of information to create a collective intelligence, delivering actionable insights, the role of the clinician as advisor and care coordinator is reinforced. In return, the human aspect of their profession is ever more valued: i.e. their capacities to listen, trust, deliver advice, empathize, and understand the global context of the patients’ lives, in order to decide the best care path.
Another positive consequence of AI is promoted by so-called network effects. The AI continues to learn as more analyzes of patient data are performed, in turn further strengthening the superior collective intelligence accessible to the entire community of users. Ultimately this means that the more clinicians use AI, the better and the more accurate the results for every patient.
Knowledge sharing was recently highlighted by the American College of Medical Genetics and Genomics (ACMG) as a successful approach to sharing clinical expertise. This means more accurate and actionable insights, particularly considering additional data sources such as phenotypic measurements, and thus a simpler and faster decision-making process to benefit both clinicians and patients.
This concept of providing easy-to-use, accurate analytical solutions, gathering and scaling a collective intelligence in a private and secure manner to the ultimate benefit of patients is at the heart of SOPHiA, the AI we have built for data-driven medicine. Genomics data interpretation that was once complex, expensive and time-consuming, is now becoming more accessible and affordable for clinicians everywhere across the globe.
This vision triggered a continuous wave of democratization of data-driven medicine over the past years and should continue to do so in the coming years. At Sophia Genetics, the continued adoption of our AI by more than 240 hospitals from 39 countries since 2014 means 80,000 patients have already benefited from more personalized, fast and accurate diagnostics in oncology, hereditary cancer, cardiology, metabolism and pediatrics.
As we commit to continue leading the democratization of data-driven medicine, we call for a continued knowledge-sharing effort so that more patients benefit from the upcoming era of real-time epidemiology.
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