Discoveries of aquifers — underground earth formations that hold water — often create excitement around their ability to ease water scarcity in a region. The United States recently announced the discovery of five aquifers in Niger, one of Africa’s most water scarce countries, containing over 600 billion cubic metres of water. To put it into perspective, Egypt’s current water demand is 114 billion cubic metres of water per year.

These are welcome announcements. Due to a changing climate and the increasing demands of a growing population, many of Africa’s surface water resources — such as dams and rivers — are facing serious risks. They’re being overused and slowly decreasing.

Alternative water sources, like aquifers, need to be explored. They are highly prevalent across the African continent, but they’re not always going to help address water scarcity. For instance, early research findings deemed Kenya’s Turkana aquifer water unfit for use due to high salinity. It’s important to bear these challenges in mind so that expectations can be managed. It is also useful for planners and governments, as they need to think of other ways around the water scarcity problem.

(Gaathier Mahed. https://theconversation.com, 21.03.2023. Adaptado.)

Discoveries of aquifers — underground earth formations that hold water — often create excitement around their ability to ease water scarcity in a region. The United States recently announced the discovery of five aquifers in Niger, one of Africa’s most water scarce countries, containing over 600 billion cubic metres of water. To put it into perspective, Egypt’s current water demand is 114 billion cubic metres of water per year.

These are welcome announcements. Due to a changing climate and the increasing demands of a growing population, many of Africa’s surface water resources — such as dams and rivers — are facing serious risks. They’re being overused and slowly decreasing.

Alternative water sources, like aquifers, need to be explored. They are highly prevalent across the African continent, but they’re not always going to help address water scarcity. For instance, early research findings deemed Kenya’s Turkana aquifer water unfit for use due to high salinity. It’s important to bear these challenges in mind so that expectations can be managed. It is also useful for planners and governments, as they need to think of other ways around the water scarcity problem.

(Gaathier Mahed. https://theconversation.com, 21.03.2023. Adaptado.)

When we think of AI, we think of robots that act like humans or computer programs that have a “conscience”. This concept is largely associated with science fiction, but it’s fast becoming a reality all around us.

These days, AI is a hot topic in multiple industries – even in clinical research. Essentially, AI, or Artificial Intelligence, is a field combining computer science with expansive datasets, which allows for machine-enabled problem solving. AI writing and art generators are the better-known examples for the general public, but it is also used in other industries, such as clinical research, which is shifting to more decentralized models, as the use of wearable medical technology has risen.

Actually, AI can support and improve the use of wearables in many ways. Besides automatically collecting and processing data inputs, it can also automate decision-making regarding device notifications. An AI program could also generate recommended patient actions based on patterns in their health data. There are several obstacles when it comes to decentralized clinical trials, one of which is data collection and processing. Since patients are off-site, they have to regularly and consciously submit their own participation data. This can bring up issues with patient compliance and data errors. CROs and medical research institutions can leverage AI to solve these issues in several ways. They can create algorithms to analyze patient data and create decisions that will achieve a desired outcome. Lastly, AI can optimize and generate notifications that prompt patients to complete electronic clinical outcome assessments (eCOA) for a more reliable data pool.

Moreover, AI programs can assist patients in submitting their data by analyzing the quality of the data prior to acceptance. For example, an AI program can evaluate an image to see whether it fits the requirements of the clinical trial. It can then prompt the patient to retake the image with recommendations regarding image quality, such as lighting or angle. This limits the amount of insufficient or substandard submissions, thereby leading to fewer data processing errors.

Of course, implementing new technologies comes with challenges and difficulties. This is especially true when it comes to a complex technology such as AI, which is still being developed and optimized. But, what is important is that existing applications of artificial intelligence in clinical practices and trials have begun changing the way research is conducted and executed. AI has been supporting, enhancing, and transforming clinical research – all to the benefit of patients all over the world.

Adapted from: vial.com/blog. Accessed June 7 2023.

When we think of AI, we think of robots that act like humans or computer programs that have a “conscience”. This concept is largely associated with science fiction, but it’s fast becoming a reality all around us.

These days, AI is a hot topic in multiple industries – even in clinical research. Essentially, AI, or Artificial Intelligence, is a field combining computer science with expansive datasets ^A) , which allows for machine-enabled problem solving. AI writing and art generators are the better-known examples for the general public, but it is also used in other industries, such as clinical research, which is shifting to more decentralized models, as the use of wearable medical technology has risen.

Actually, AI can support and improve the use of wearables in many ways ^B). Besides automatically collecting and processing data inputs, it can also automate decision-making regarding device notifications. An AI program could also generate recommended patient actions based on patterns in their health data. There are several obstacles when it comes to decentralized clinical trials, one of which is data collection and processing. Since patients are off-site, they have to regularly and consciously submit their own participation data. This can bring up issues with patient compliance and data errors. CROs and medical research institutions can leverage AI to solve these issues in several ways. They can create algorithms to analyze patient data and create decisions that will achieve a desired outcome. Lastly, AI can optimize and generate notifications that prompt patients to complete electronic clinical outcome assessments (eCOA) for a more reliable data pool ^C).

Moreover, AI programs can assist patients in submitting their data by analyzing the quality of the data prior to acceptance ^D). For example, an AI program can evaluate an image to see whether it fits the requirements of the clinical trial. It can then prompt the patient to retake the image with recommendations regarding image quality, such as lighting or angle. This limits the amount of insufficient or substandard submissions, thereby leading to fewer data processing errors.

Of course, implementing new technologies comes with challenges and difficulties. This is especially true when it comes to a complex technology such as AI, which is still being developed and optimized. But, what is important is that existing applications of artificial intelligence in clinical practices and trials have begun changing the way research is conducted and executed. AI has been supporting, enhancing, and transforming clinical research – all to the benefit of patients all over the world.

Adapted from: vial.com/blog. Accessed June 7 2023.

When we think of AI, we think of robots that act like humans or computer programs that have a “conscience”. This concept is largely associated with science fiction, but it’s fast becoming a reality all around us.

These days, AI is a hot topic in multiple industries – even in clinical research. Essentially, AI, or Artificial Intelligence, is a field combining computer science with expansive datasets, which allows for machine-enabled problem solving. AI writing and art generators are the better-known examples for the general public, but it is also used in other industries, such as clinical research, which is shifting to more decentralized models, as the use of wearable medical technology has risen.

Actually, AI can support and improve the use of wearables in many ways. Besides automatically collecting and processing data inputs, it can also automate decision-making regarding device notifications. An AI program could also generate recommended patient actions based on patterns in their health data. There are several obstacles when it comes to decentralized clinical trials, one of which is data collection and processing. Since patients are off-site, they have to regularly and consciously submit their own participation data. This can bring up issues with patient compliance and data errors. CROs and medical research institutions can leverage AI to solve these issues in several ways. They can create algorithms to analyze patient data and create decisions that will achieve a desired outcome. Lastly, AI can optimize and generate notifications that prompt patients to complete electronic clinical outcome assessments (eCOA) for a more reliable data pool.

Moreover, AI programs can assist patients in submitting their data by analyzing the quality of the data prior to acceptance. For example, an AI program can evaluate an image to see whether it fits the requirements of the clinical trial. It can then prompt the patient to retake the image with recommendations regarding image quality, such as lighting or angle. This limits the amount of insufficient or substandard submissions, thereby leading to fewer data processing errors.

Of course, implementing new technologies comes with challenges and difficulties. This is especially true when it comes to a complex technology such as AI, which is still being developed and optimized. But, what is important is that existing applications of artificial intelligence in clinical practices and trials have begun changing the way research is conducted and executed. AI has been supporting, enhancing, and transforming clinical research – all to the benefit of patients all over the world.

Adapted from: vial.com/blog. Accessed June 7 2023.

When we think of AI, we think of robots that act like humans or computer programs that have a “conscience”. This concept is largely associated with science fiction, but it’s fast becoming a reality all around us.

These days, AI is a hot topic in multiple industries – even in clinical research. Essentially, AI, or Artificial Intelligence, is a field combining computer science with expansive datasets, which allows for machine-enabled problem solving. AI writing and art generators are the better-known examples for the general public, but it is also used in other industries, such as clinical research, which is shifting to more decentralized models, as the use of wearable medical technology has risen.

Actually, AI can support and improve the use of wearables in many ways. Besides automatically collecting and processing data inputs, it can also automate decision-making regarding device notifications. An AI program could also generate recommended patient actions based on patterns in their health data. There are several obstacles when it comes to decentralized clinical trials, one of which is data collection and processing. Since patients are off-site, they have to regularly and consciously submit their own participation data. This can bring up issues with patient compliance and data errors. CROs and medical research institutions can leverage AI to solve these issues in several ways. They can create algorithms to analyze patient data and create decisions that will achieve a desired outcome. Lastly, AI can optimize and generate notifications that prompt patients to complete electronic clinical outcome assessments (eCOA) for a more reliable data pool.

Moreover, AI programs can assist patients in submitting their data by analyzing the quality of the data prior to acceptance. For example, an AI program can evaluate an image to see whether it fits the requirements of the clinical trial. It can then prompt the patient to retake the image with recommendations regarding image quality, such as lighting or angle. This limits the amount of insufficient or substandard submissions, thereby leading to fewer data processing errors.

Of course, implementing new technologies comes with challenges and difficulties. This is especially true when it comes to a complex technology such as AI, which is still being developed and optimized. But, what is important is that existing applications of artificial intelligence in clinical practices and trials have begun changing the way research is conducted and executed. AI has been supporting, enhancing, and transforming clinical research – all to the benefit of patients all over the world.

Adapted from: vial.com/blog. Accessed June 7 2023.