Artificial Intelligence (AI) the powerful next generation of health

Artificial intelligence for global health

Artificial intelligence for global health (AI) has demonstrated great progress in the detection, diagnosis, and treatment of diseases. Deep learning has enabled applications with performance levels approaching those of trained professionals in tasks including the interpretation of medical images and discovery of drug compounds.

The ability of AI to deliver on its promises, however, depends on successfully resolving the ethical and practical issues identified, including that of exploitability and algorithmic bias.

What exactly is Artificial Intelligence (AI)?

According to The Newscientist web, Artificial intelligence is a potentially world-changing technology.
It could help cure cancers, control autonomous cars, and augment human intelligence. AI simply means the software used by computers to mimic aspects of human intelligence.
 
Artificial intelligence for global health is an overarching term used to describe the use of machine-learning algorithms and software, or artificial intelligence (AI), to mimic human cognition in the analysis, presentation, and comprehension of complex medical and health care data.

Artificial intelligence is becoming a transformational force in healthcare

Case n#1 of Artificial intelligence for global health

Nature.com has published a scientific report about ” Blood pressure measurements with the OptiBP smartphone app”, saying that High blood pressure (BP) remains the leading risk factor for death and disability in both high and low-income countries.

Its complications are responsible for the deaths of approximately ten million people annually, a 50% increase over the estimates from 1990.

By 2025, the number of people suffering from hypertension will reach 1.5 billion. The impact of this disease represents a daunting burden to any healthcare system.

WHO issues first global report on Artificial Intelligence (AI) in health

Artificial Intelligence (AI) holds great promise for improving the delivery of healthcare and medicine worldwide, but only if ethics and human rights are put at the heart of its design, deployment, and use, according to new WHO guidance published today.

The report, Ethics, and governance of artificial intelligence for health, is the result of 2 years of consultations held by a panel of international experts appointed by WHO.

“Like all new technology, artificial intelligence holds enormous potential for improving the health of millions of people around the world, but like all technology, it can also be misused and cause harm,” said Dr. Tedros Adhanom Ghebreyesus, WHO Director-General. “

This important new report provides a valuable guide for countries on how to maximize the benefits of AI while minimizing its risks and avoiding its pitfalls.”

Artificial intelligence can be, and in some wealthy countries is already being used to:

  • improve the speed and accuracy of diagnosis and screening for diseases;
  • assist with clinical care; strengthen health research and drug development.
  • support diverse public health interventions, such as disease surveillance, outbreak response, and health systems management.

Test case: OptiBp

Mobile health diagnostics have been shown to be effective and scalable for chronic disease detection and management.

By maximizing the smartphones’ optics and computational power, they could allow the assessment of physiological information from the morphology of pulse waves and thus estimate cuffless blood pressure (BP).

Methodology for training OptiBP

We trained the parameters of an existing pulse wave analysis algorithm (oBPM), previously validated in anesthesia on pulse oximeter signals, by collecting optical signals from 51 patients fingertips via a smartphone while simultaneously acquiring BP measurements through an arterial catheter.

We then compared smartphone-based measurements obtained on 50 participants in an ambulatory setting via the OptiBP app against simultaneously acquired auscultatory systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure (MBP) measurements.

Patients were normotensive (70.0% for SBP versus 61.4% for DBP), hypertensive (17.1% vs. 13.6%) or hypotensive (12.9% vs. 25.0%).

The difference in BP (mean ± standard deviation) between both methods were within the ISO 81,060–2:2018 standard for SBP (− 0.7 ± 7.7 mmHg), DBP (− 0.4 ± 4.5 mmHg), and MBP (− 0.6 ± 5.2 mmHg). These results demonstrate that BP can be measured with accuracy at the finger using the OptiBP smartphone app.

This may become an important tool to detect hypertension in various settings, for example in low-income countries, where the availability of smartphones is high but access to health care is low.

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