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Diabetic hypoglycemia occurs when someone with diabetes doesn’t have enough sugar (glucose) in his or her blood. Glucose is the main source of fuel for the body and brain, so you can’t function well if you don’t have enough.

For many people, low blood sugar (hypoglycemia) is a blood sugar level below 70 milligrams per deciliter (mg/dL) or 3.9 millimoles per liter (mmol/L). But your numbers might be different. Ask your health care provider about the appropriate range to keep your blood sugar (target range).

• Neurogenic or neuroglycopenic symptoms of hypoglycemia may be categorized as follows:
• Neurogenic (adrenergic) (sympathoadrenal activation)symptoms: Sweating, shakiness, tachycardia, anxiety, and a sensation of hunger
• Neuroglycopenic symptoms: Weakness, tiredness, or dizziness; inappropriate behavior (sometimes mistaken for inebration); difficulty with concentration; confusion; blurred vision; and, in extreme cases, coma and death

Please pay attention to the early warning signs of hypoglycemia and treat low blood sugar as soon as possible. You can raise your blood sugar quickly by eating or drinking a simple sugar source, such as glucose tablets, hard candy or fruit juice. Could you tell family and friends what symptoms to look for and what to do if you’re not able to treat the condition yourself?

Before people with diabetes experience hypoglycemia while driving, they could be warned by an AI in the future. Researchers from Munich and Switzerland have successfully tested such an application.

To warn drivers who have diabetes in good time about hypoglycemia in the future, researchers are working on a system that uses artificial intelligence (AI). Scientists from the Ludwig Maximilian University of Munich (LMU) and researchers from ETH Zurich, the Berner Inselspital and the University of St. Gallen are involved.

Test drives before and after induced hypoglycemia

The researchers tested their AI model in a large-scale driving test on a military site in Thun, Switzerland. The driving patients were each accompanied by a driving instructor next to them and two or three medical professionals in the back seat.

After initially driving with normal blood sugar levels, they administered continuously insulin to the driver so that the blood sugar level became lower and lower. The corresponding data was recorded to develop an AI model.

Analysis of the driving behavior of hypoglycemic patients

Simon Schallmoser, a doctoral student at the LMU, is writing a doctoral thesis on this topic and has evaluated the driving data for his AImodel, as well as the head and eye movements recorded by camera of those with artificial hypoglycemia who were behind the wheel.

When a person experiences hypoglycemia, their movements change. To be more precise, the look and position of the head become a little more monotonous. People with hypoglycemia tend to look in the direction of gaze for longer, and when they change their direction of gaze , it happens more quickly.

They are no longer quite as forward-looking, and this can also be measured using the car’s driving signals, explains Schallmoser: “For example, we noticed that patients with low blood sugar levels make fewer small corrections when steering, which we know from driving a car, but rather change the direction of travel very abruptly.”

Tests in real road traffic are still pending

The AI ​​application was tested on a test track at Anairfield, where driving in city traffic, on country roads and on the motorway was simulated with 30 patients. That’s enough for the AI ​​application to be meaningful, says Simon Schallmoser. However , according to Schallmoser, new experiments would have to be carried out before it could actually be used in real road traffic, as the test route only had limited significance for real road traffic.

The researcher explains that further studies will be necessary before it is ready for the market. However, the first tests as to whether artificial intelligence detects hypoglycemia have already been very promising.“We trained the model on patients and then tested it on other patients in the same study,” says Simon Schallmoser. “In machine learning, we talk about the fact that training and test data sets must not match; that is, the patients must not overlap. That’s how we tested it, and it worked very well. ”

Other possible uses are conceivable

Further tests are required, as well as cooperation with interested car manufacturers to install such systems in vehicles. This software upgrade is for well-equipped, modern cars, as the camera to detect drowsiness is already on board.

The question remains whether the AI ​​​​application could be used for other purposes, such as detecting hypoglycemia and perhaps alcohol consumption. These tests are still pending. A large supplier was already involved in the test drives.

Detecting Diabetic Eye Disease Through AI Learning

Researchers at the Google Brain initiative have utilized “deep learning” methods to develop a self-optimizing algorithm that can analyze large quantities of fundus photographs and automatically identify diabetic retinopathy (DR) and diabetic macular edema (DME) with a high level of precision.

When the performance of the screening algorithm was evaluated by the researchers using 2 groups of images (N = 11,711), it demonstrated a sensitivity of 96.1% and 97.5% and a specificity of 93.9% for DR and DME, respectively.1

Peter A. Karth, MD, MBA, a vitreoretinal subspecialist in Eugene, Ore., and at Stanford University, who is a consultant to the Google Brain project, acknowledged the achievement, stating, “It’s a real accomplishment that Google was able to get high sensitivity and specificity at the same time—meaning that not only is this algorithm missing very few people who have disease, but it is also unlikely to overdiagnose disease.”

The algorithm operates based on deep machine learning, which is a form of artificial intelligence (AI) technology where a neural network “learns” to carry out a task through repetition and self-correction.

In this instance, the authors noted that the computerized algorithm was trained using 128,175 human-graded fundus images showing different levels of diabetic retinal disease. The authors explained, “Then, for each image, the severity grade given by the [algorithm] is compared with the known grade from the training set, and parameters … are then modified slightly to decrease the error on that image.” They added, “This process is repeated for every image in the training set many times over, and the [algorithm] ‘ learns’ how to accurately compute the diabetic retinopathy severity from the pixel intensities of the image for all images in the training set.”

According to Dr. Karth, the algorithm is effective despite not being designed to specifically search for the lesion-based features that a human would look for on fundus images. He stated, “What’s so exciting with deep learning is that we’re not actually yet sure what the system is looking at. All we know is that it’s arriving at a correct diagnosis as often as ophthalmologists are.”

Ehsan Rahimy, MD, a Google Brain consultant and vitreoretinal subspecialist in practice at the Palo Alto Medical Foundation, in Palo Alto, Calif., expressed similar sentiments, stating, “We don’t entirely understand the path that the system is taking. may very well be seeing the same things we’re seeing, like microaneurysms, hemorrhages, or neovascularization.”

AI Will Not Replace Doctors’ Intelligence

Dr. Karth and Dr. Rahimy highlighted that although additional work is required before the algorithm is ready for clinical use, the ultimate objective is to enhance access to and reduce the cost of screening and treatment for diabetic eye disease, particularly in under-resourced environments.

Dr. Rahimy emphasized, “Anytime you talk about machine learning in medicine, the knee-jerk reaction is to worry that doctors are being replaced. But this is not going to replace doctors. In fact, it’s going to increase the flow of patients with real disease who needs real treatments.”

Dr. Karth added, “This is an important first step toward dramatically lowering the cost of screening for diabetic retinopathy and, therefore, dramatically increasing the number of people who are screened.”

The Role of AI in Healthcare

AI has emerged as a revolutionary tool in healthcare, and with its ability to process extensive data, it has the potential to transform the accuracy and effectiveness of diagnostics and predictive decision-making. While AI offers numerous benefits and possibilities for diabetes research, diagnosis, and prognosis, it also comes with limitations.

Understanding AI in Healthcare

Artificial intelligence involves the simulation of human intelligence in machines programmed to think and learn like humans. In the healthcare sector, AI technologies, such as machine learning and deep learning, have made significant strides due to enhanced computer speed and increased computational resources.

Machine learning entails training algorithms to recognize patterns and make data-based predictions, commonly known as predictive analytics. On the other hand, deep learning utilizes neural networks to process intricate information and extract meaningful insights. These AI technologies enable healthcare professionals to analyze extensive datasets and derive valuable conclusions to enhance patient care.

AI’s efficacy lies in its ability to identify diabetes-related complications using comprehensive datasets and advanced algorithms.

How AI Can Enhance Diabetes Care

Accurate and timely diagnosis and treatment are crucial for effective diabetes management. AI’s effectiveness stems from its capability to identify diabetes-related complications using extensive datasets and advanced algorithms.

For instance, AI-based medical devices have been authorized for automated retinal screening to identify diabetic retinopathy (DR) from fundus images. The IDx-DR device, approved by the FDA for DR diagnosis, can provide a diagnosis without requiring professional judgment from an ophthalmologist. Its use has been especially beneficial for rural communities with limited access to specialized healthcare professionals.

AI, with its capability to fine-tune insulin doses and enhance decision-making processes, can significantly aid in clinical treatment. Systems like Advisor Pro, which employs AI algorithms to analyze continuous glucose monitoring (CGM) and self-monitoring blood glucose (SMBG) ) data, can facilitate remote insulin dose adjustments. This technology empowers healthcare professionals to make informed decisions to support their patients’ self-care.

AI can also help with risk stratification, allowing healthcare professionals to identify high-risk individuals and offer targeted interventions. Machine learning algorithms can assess patient data, including medical history, lifestyle factors, and genetic markers, to predict the likelihood of developing diabetes or its complications. This information can guide preventive measures and personalized treatment plans.

It is crucial to understand how AI reaches its conclusions to gain trust and acceptance from healthcare professionals and patients.

Constraints and difficulties of AI

While AI holds significant promise in diabetes research and management, it is important to recognize its limitations and challenges. One primary concern is the interpretability and explainability of AI algorithms. Unlike traditional statistical models, AI algorithms can be perceived as “black boxes” due to their complex decision-making processes. It is critical to understand how AI arrives at its conclusions to gain trust and acceptance from healthcare professionals and patients.

Addressing the challenges of AI in diabetes management, such as the requirement for high-quality, diverse, and well-annotated datasets, necessitates a collaborative effort. AI heavily relies on training data to learn patterns and make accurate predictions. However, data bias and limited access to comprehensive datasets can impede the performance and generalizability of AI models. Therefore, it is crucial for researchers, healthcare institutions, and regulatory bodies to collaborate to ensure robust and representative data availability.

Furthermore, regulatory frameworks must keep pace with the rapid advancements in AI technology. Clear guidelines and standards are needed to ensure safe and ethical use of AI in healthcare. Other considerations such as data privacy, security, and patient confidentiality are also crucial to build public trust in AI-driven healthcare solutions.

As technology and medical science progress, the accuracy and predictive performance of AI algorithms will also improve.

Looking ahead with AI

Despite the challenges, ongoing research and innovation in AI hold significant promise for diabetes care. As technology and medical science advance, the accuracy and predictive performance of AI algorithms will also improve.

Organized data and ample computational capacity will optimize AI’s forecasting capabilities, leading to more accurate disease prediction models for diabetes. This progress instills hope for a future where AI can significantly improve patient outcomes and transform diabetes management.

As we look to the future, collaboration between researchers, healthcare professionals, and technology experts will be crucial in harnessing AI’s full potential in diabetes management. By overcoming challenges and leveraging AI’s power, we can pave the way for a future where diabetes is better understood , managed, and ultimately prevented.

With the rise of digitalization, we have observed diabetes management expanding beyond commonly used devices to smartphone apps.

Innovations in diabetes management technologies can offer more effective and manageable treatment options, ultimately transforming the landscape of diabetes care. With the rise of digitalization, we have seen diabetes management expanding beyond commonly used devices to smartphone applications – apps for short.

An app is self-contained software crafted for a mobile device – smartphones, tablets, laptops, or desktop computers – that enables users to carry out specific tasks. Apps are particularly convenient when used on mobile devices. They can be utilized offline, providing access to information and features even without an internet connection. Mobile apps can also send notifications to users, providing real-time updates.

Popular features in diabetes mobile apps

A range of features found in diabetes mobile apps can make diabetes management more convenient. These features enable users to record insulin, physical activity, and carbohydrate intake, and monitor crucial health data, all while gathering data directly from continuous glucose monitors (CGMs). Some even offer distinct features, such as low blood glucose alerts.

Alternative types of applications provide features for diabetes. They connect various blood glucose meters (BGMs), continuous glucose monitors (CGMs), and insulin pumps to create detailed charts of blood glucose levels, insulin managing extensive data. Users can create personalized care plans in collaboration with their diabetes care team. Additionally, these apps are widely available on different platforms, ensuring accessibility for users regardless of location or device.

There are manageable potential risks, but the benefits and conveniences offered by diabetes apps outweigh the drawbacks, making them valuable in diabetes care.

While diabetes apps offer many advantages, they also come with potential drawbacks. These include the need for frequent updates as the app evolves and a necessity for greater regulation to prevent bugs or security risks. However, it’s crucial to note that these potential risks can be managed, and the benefits and conveniences offered make diabetes apps valuable in diabetes care.

Selecting a diabetes management app should involve following good practices. Trying out multiple apps before deciding on the most suitable one is recommended. Consider your preferences, goals, and the need for a personalized diabetes management plan. Healthcare providers can often assist their patients in understanding how to use an app, interpreting data, and providing guidance on any limitations, ensuring an informed decision.

Recent technological advancements in diabetes management have made it easier to synchronize automated insulin delivery systems (AID) and continuous glucose monitors (CGMs) with an app. AID systems combine an insulin pump and CGM to help people with diabetes monitor their blood glucose levels. intelligent algorithm links the two devices, enabling them to exchange data. AIDs can improve glycemic control through real-time responses, ultimately reducing the burden of manual insulin dosing.

For diabetes management, there are electronic platforms known as Diabetes Management Platforms (DMPs) which can aid people with diabetes. DMPs collect data from diabetes devices (BGM, CGM, or insulin pump) through a synced mobile app, and this data can also be accessed online for manual logging.

Diabetes management platforms utilize AI and CGMs to provide personalized management strategies by predicting blood glucose levels and optimizing insulin dosages. They can also address accessibility issues by ensuring the latest diabetes technology is available from the time of diagnosis. DMPs using AI incorporate an algorithm-powered dashboard that consolidates data from different diabetes devices and presents it in a user-friendly manner for healthcare providers, enhancing diabetes care and management.

The future of DMPs looks promising, with continuous technological advancements offering improved app functionalities. By transitioning from traditional pencil-logbook methods to sophisticated data logging and analysis, DMPs have the potential to revolutionize diabetes management. Furthermore, these platform advancements can support healthcare providers in guiding their patients toward practical diabetes management tools.

The use of artificial intelligence (AI) in diabetes care has been focused on early intervention and treatment management. Notably, this usage has expanded to predict an individual’s risk of developing type 2 diabetes. A scoping review of 40 studies by Mohsen et al. shows that while most studies used single AI models, those that used multiple types of data were more effective. However, creating and determining the performance of these multi-faced models can be challenging due to the many factors involved in diabetes.

For both single and multi-faced models, concerns exist regarding bias due to the lack of external validations and representation of race, age, and gender in training data. Developing new technologies, especially for entrepreneurs and innovators, in the areas of data quality and evaluation standardization is crucial. Collaboration among providers, entrepreneurs, and researchers must be prioritized to ensure that AI in diabetes care provides quality and equitable patient care.

Introduction

Given the urgent need to address the increasing incidence and prevalence of diabetes on a global scale, promising new applications of artificial intelligence (AI) for this chronic disease have emerged. These applications encompass the development of predictive models, risk stratification, evaluation of novel risk predictors, and therapeutic management.

So far, most FDA-approved AI tools have been designed for early intervention and treatment management. Several of these tools are currently used in clinical diabetes care. For early intervention, in 2018, the FDA approved the autonomous AI system Digital Diagnostics, which demonstrated high diagnostic accuracy in recognizing diabetes retinopathy in retinal screening images.

The Guardian Connect System, which utilizes AI technology, was approved by the FDA in the same year to analyze biomedical data and forecast a hypoglycemic attack one hour in advance. Subsequently, the FDA has sanctioned AI technologies aiding in optimizing insulin dosing and therapy for patients .

AI is now being used to anticipate an individual’s risk of developing type 2 diabetes (T2DM) and potential complications, aside from intervention and treatment. Recognizing high-risk individuals and customizing prevention strategies and targeted treatments could delay or prevent the onset of diabetes and future health complications.

A scoping review by Mohsen et al. examined 40 studies that looked into AI-based models for diabetes risk prediction. Most studies gauged the performance of the area using the area under the curve (AUC) metric, a common metric in machine learning algorithms. AUC value of 1 denotes a perfect model.

The majority of these models were classical machine learning models with electronic health records as the primary data source. Although a limited number of studies (n = 10) employed multimodal approaches, they outperformed unimodal models (n = 30).

For instance, one multimodal approach found that a model integrating genomic, metabolomic, and clinical risk factors was superior in predicting T2DM (AUC of 0.96) compared to genomics-only (AUC of 0.586) and clinical-only models (AUC of 0.798).

However, developing multimodal models is highly time-consuming, making it challenging to scale such models easily. Moreover, integrating data sources can complicate the understanding of interactions among modalities and the rationale behind predictions, resulting in a scarcity of multimodal AI models for T2DM.

Although the review by Mohsen et al. suggests promising AI technologies for T2DM risk prediction, the findings should be approached cautiously. Determining the best-performing model is challenging due to the influence of various input risk predictors for diabetes.

For example, the XGBoost algorithm was used in three unimodal studies but yielded widely disparate AUC values ​​(0.91, 0.83, and 0.679) due to variations in risk predictors and datasets.

Moreover, there are concerns regarding bias stemming from the demographic representation across models, with many showing imbalanced gender, ethnicity, and age. Most studies did not evaluate the algorithm’s performance across different demographic groups, hence perpetuating existing health inequities for already at-risk populations.

To ensure demographic representation in datasets, it is necessary to implement policies that require mandatory representation criteria for approval and adoption. It is important to integrate appropriate evaluation metrics, such as using Quality Assessment of Diagnostic Accuracy Studies (QUADAS) AI frameworks to evaluate a model’s risk of bias. External validation is also crucial to ensure the models’ generalizability beyond specific training datasets.

The QUADAS AI tool is a tool based on evidence designed to evaluate bias risk—related to patient selection, diagnostic test interpretation, and choice of reference standard—and applicability—generalizability of a study’s findings to the intended population—of diagnostic accuracy studies AI Adopting a comprehensive approach will ensure the use of fair and impartial AI models in order to prevent worsening existing health discrepancies.

Coming Soon

AI tools in diabetes care, specifically those trained with a multimodal approach, have promising applications in risk prediction. However, as unimodal approaches are still more prevalent, there exists untapped potential in employing more precise tools that match the standards of clinical care patients deserve. Innovative solutions are required on two fronts—data quality and standardized assessment metrics.

To build accurate tools, it is essential to have comprehensive and diverse datasets to train models. Especially as health data continues to be gathered to create robust datasets, there is a need to organize and structure the data for potential compatibility and interoperability when developing multimodal algorithms Universal evaluation protocols are also required to minimize the perpetuation of health inequalities.

The widespread and rapid adoption of AI in healthcare cannot happen until the issues related to data quality and bias are addressed—making these two aspects prime areas of development for innovations and new technologies from the private sector. Solutions that foster collaboration and transparency on these two fronts could draw inspiration from structures in other AI, such as open-source fields platforms, ethical review processes, and enforcement of bias testing in order to uphold a higher standard of practice.

In order to ensure that patient care is the primary focus of innovative AI tools in diabetes care, solutions must stem from collaborative efforts with all stakeholders—clinicians, researchers, policymakers, and entrepreneurs—as we continue to drive progress in the field of AI and diabetes.

Artificial intelligence and diabetes are two topics that are dear to me. This is why, in celebration of World Diabetes Day, I have chosen to share the numerous fascinating ways AI is assisting the medical field in the battle against the disease.

Whether you have diabetes or not, I am confident you will appreciate the innovative capabilities of humankind.

Acknowledging World Diabetes Day

On Saturday, November 14th, the world turned its attention to World Diabetes Day: an annual global campaign aimed at raising awareness about diabetes. The International Diabetology Federation established the campaign in 1991.

They picked November 14th because it marks the birthday of Frederick Banting: the individual who discovered insulin.

“Some 422 million individuals across the globe have diabetes.” — World Health Organization

Diabetes is significant to me both as someone dealing with the condition and as a professional, as my team and I continue to develop Suguard: an AI-based smartphone app designed to make daily life easier for individuals with the condition.

Suguard is an internal project we’ve been working on since 2014, the year we established DiabetesLab: our second company focused on creating advanced software that aids individuals in managing an illness using AI.

Suguard is not only my brainchild but also my aspiration. As someone grappling with the condition, I see a substantial need for such a personalized application. My experiences have been the driving force behind my quest to find a solution to help me stay active and enjoy sports.

Individuals with diabetes often require extensive treatment and exceptional care, especially during physical activities. But that does not make it impossible. And I am firm in my belief that I am living proof that individuals with the condition can still engage in sports at a high level .

I am speaking about this because my desire to engage in sports compelled me to create a solution that would assist me. And I hope that soon, it will be the most useful app globally for individuals with Type 1 diabetes.

Should this pique your interest, you can find out more about the project in my article on How AI and Data Science Can Help Manage Diabetes in Everyday Life). However, today my focus is not on Suguard.

Instead, I am here to share other AI-based solutions that are aiding individuals with diabetes in managing the condition.

I hope you appreciate the insights.

Five Methods by Which Artificial Intelligence Enhances Diabetes Care

There are numerous ways to utilize AI for diabetes. The following five are the most innovative applications I am aware of; if you know of any others, please send them my way.

1. Diagnosis of Diabetic Retinopathy

Physicians are effectively utilizing deep learning to automate the diagnosis of diabetic retinopathy: a complication linked to diabetes that can lead to vision loss.

Experts are employing AI-based screening to identify and track occurrences of diabetic retinopathy, with 96% of patients being satisfied with the service. The technology utilizes convolutional neural networks to identify potential issues on a patient’s retina, achieving accuracy levels of 92.3% and specificity levels of 93.7%.

2. Modeling Disease Risk

Healthcare institutions leverage machine learning to create models that predict the likelihood of diabetes within specific population groups. This involves analyzing factors such as lifestyle, physical and mental well-being, and social media activity.

A dataset of 68,994 individuals was utilized to train the algorithm for predicting diabetes, resulting in a highly accurate prediction model. The software not only assesses the risk of long-term complications like Diabetic Retinopathy and cardiovascular or renal issues but also considers short-term concerns such as hypoglycemia.

3. Self-Management of Diabetes

Effective self-management plays a pivotal role in diabetes care. AI has empowered patients to take charge of their own health by using personal data to tailor their lifestyle and essentially assume the role of an at-home healthcare provider.

Artificial intelligence allows individuals to make informed decisions regarding dietary choices and physical activity levels. Smartphone applications like Suguard simplify self-management through real-time analysis of food’s calorific value.

4. Advanced Genomic Studies

Genetic makeup holds valuable insights into one’s health. Advanced molecular phenotyping, epigenetic changes, and the rise of digital biomarkers are aiding medical professionals in enhancing the diagnosis and management of conditions such as diabetes by harnessing genomics.

Microbiome data has provided a wealth of microbial marker genes that can predict the likelihood of diabetes and even guide treatment. Furthermore, research has uncovered over 400 genetic signals that indicate the risk of developing diabetes.

5. Monitoring Complications

Diabetes can lead to various common complications, including vascular disorders (manifesting as strokes, blood clots, or arterial disease) and peripheral neuropathies (resulting in weakness, numbness, and pain, particularly in the hands and feet).

Similar to the use of machine learning in Diabetic Retinopathy diagnosis, AI can aid in identifying and monitoring other related issues. For instance, an app named FootSnap is capable of detecting inflammation and predicting potential foot ulcers.

AI’s Impact on Lives

Artificial intelligence has brought about a significant transformation in the daily lives of individuals affected by diabetes. Abundant disease-related data is not only enhancing self-management but also customizing treatment plans, with a growing number of advanced solutions entering the field each year.

How will AI transform medical diagnostics in 2024?

The healthcare sector will undergo a revolution with the introduction of AI in medical diagnostics in 2024.

Advanced machine learning algorithms will be swiftly integrated into healthcare systems, enabling medical professionals to analyze globally large volumes of patient data to identify patterns that will not only enhance the accuracy of their diagnoses but also help them discover broader and potentially previously unknown connections, leading to earlier detection.

The end result will be improved patient outcomes, reduced workload for healthcare workers, and potentially the identification of new diagnostic techniques.

Here are a few ways we can anticipate the integration of AI into the diagnostic process in 2024:

1. AI-generated and self-diagnosis

Self-diagnosis refers to individuals attempting to diagnose their own illnesses based on their symptoms, typically by consulting online resources. Search engines and social media have historically played a significant role in self-diagnosis, and up to one-third of people in the United States have used the internet to diagnose their own ailments.

Self-diagnosis can benefit the healthcare sector – if patients can accurately diagnose their symptoms, it can alleviate the burden on general practitioners and lead to quicker, better outcomes.

However, one of the major drawbacks of using the internet for self-diagnosis is that patients often misdiagnose their illness, either by misunderstanding the link between a symptom and the associated disease, overemphasizing the significance of one symptom, or overlooking a symptom altogether.

Confirmation bias also plays a significant role: if a patient is convinced they have a specific illness, they may be inclined to omit or fabricate symptoms to align with the diagnostic criteria. As a result, approximately 34% of all self-diagnoses are incorrect, which can lead to complications later on.

This is where Artificial Intelligence comes in. New AI chatbots will have access to an extensive collection of medical literature as well as the ability to develop comprehensive understandings of symptoms and rapidly process data to generate potential diagnoses. This will enable patients to describe their symptoms and receive immediate feedback, aiding them in self-diagnosing with more accurate results.

2. Utilizing big data for predictive analytics

The healthcare sector already accounts for over one-third (33%) of all data worldwide. This data is growing at an exponential rate, faster than in any other sector. In fact, a single hospital in the USA generates approximately 137 terabytes of new data per day. Given the vastness of this pool, it would be practically impossible for human knowledge workers to derive meaningful insights from it.

Fortunately, AI enables the automated handling of healthcare data, including processing and reporting. Through supervised learning and the creation of deep neural networks, healthcare professionals are training AI to understand and interpret healthcare data in order to enhance diagnostics. This involves analyzing extensive data sets , identifying trends within the data, comparing data with other population-wide and historical data sets, and cross-referencing results with decades’ worth of medical literature. Processes that would have taken human experts weeks or even months to complete can now be accomplished by AI in minutes.

At the beginning of 2024, AI is already being utilized in various diagnostic methods, not just for processing textual and numerical data, but also in medical imaging research (such as X-rays, CT scans, and MRIs). For example, by examining the buildup of plaque in a patient’s arteries across sets of computed tomography angiography (CTA) images, researchers at Cedars Sinai have developed an AI model capable of identifying patients at risk of heart attacks.

In addition, researchers are exploring the use of AI in big data analysis to create diagnostic models for conditions like breast cancer, dementia, diabetes, and kidney disease. The goal is for these AI models to automatically identify patients’ risks of various illnesses and initiate treatment before these conditions become critical. In addition to potential cost savings, these preventive treatments could potentially save millions of lives each year.

3. Remote patient monitoring

Another area where AI is impacting the diagnostic process is remote patient monitoring. Currently, triage heavily relies on patients presenting themselves to a healthcare professional while displaying symptoms. This can lead to errors, such as when the symptoms presented at the time do not align with the diagnosis, when the patient is asymptomatic, when the severity of symptoms is misinterpreted, resulting in a more urgent or less urgent response than necessary, or when a diagnosis is missed entirely.

These errors and misdiagnoses can, in turn, lead to wasted time, effort and money. Misdiagnoses are believed to cost the US healthcare industry around US$100 billion per year.

One part of the solution may lie in AI-powered remote patient monitoring, allowing patients to be monitored over time in order to keep track of changes in their health. Remote patient monitoring could pave the way towards more accurate diagnoses by tracking the development, changes , and severity of symptoms over a sustained period of time using a variety of AI-augmented tools, including wearable devices, sensors, and patient-reported information.

Not only could this system be used to catch symptoms that may otherwise be missed, it offers the potential for doctors to spot symptoms earlier, leading to faster diagnoses and potentially better patient outcomes. Better still, in the search for one diagnosis, medical professionals may be able to spot other diagnoses, saving the patient from having to attend triage multiple times.

4. New diagnostic research

Artificial intelligence can now enable healthcare practitioner to identify new diagnostic models. This could apply both to never-before-identified illnesses or variations of existing illnesses, and to new diagnostic frameworks for well-known illnesses.

AI’s ability to process huge segments of data will allow medical experts to spot new patterns and trends developing across a population. This could lead to many interesting benefits. For instance, with virulent diseases, AI will be able to track the spread of these diseases and allow experts to identify how the illness moves from person to person, how quickly it can spread, time to incubation and appearance of first symptoms, and so on.

This methodology was effectively used during the recent COVID-19 pandemic. AI helped to model disease clusters, predicting the likely spread of the illness throughout a given population, and thus informed healthcare experts as to what would be the best possible response.

This led to the development of AI-influenced contact tracing (identifying likely exposures), monitoring and early diagnosis (the ability to work backwards to identify first symptoms), and telemedicine responses (used to inform the likelihood of probable diagnosis without needing to refer individual patients to a healthcare practitioner, thus reducing workload and burden).

Artificial intelligence will bring new, streamlined ways of working to the practice of medical diagnostics.

As we’ve seen, AI has the potential to:

• Speed ​​up the diagnostic process, relieving the pressure on the medical professionals involved in triage
  Allow for earlier diagnosis, both by identifying symptoms that may otherwise go unnoticed, and through patient monitoring, which enables illnesses to be identified even before a patient presents at triage
• Improve the accuracy of diagnoses, by comparing symptoms against a vast compendium of medical literature and big data gathered from other sources to provide suggestions that can be confirmed by a professional
• Model trends across a population by analyzing large data sets and identifying patterns
• Reduce the burden on healthcare workers, leading to cost savings and freeing up experts’ time and resources for more urgent cases
• AI will have a profound impact on the healthcare sector, helping to improve both the efficiency and the quality of medical diagnostics and hopefully producing better outcomes for patients.

However, the rapid development of AI and its integration into the healthcare sector is not without its challenges, some of which include:

Potential for large-scale inaccuracies

Artificial intelligence is a learning model, and much of this learning comes from human-generated data. Indeed, AI itself is programmed by humans. This brings about the risk of inaccuracies, both in the fundamental make-up of AI, and in its ability to process data. AI is also unable to discriminate between good data and bad data, running the risk that even a minor inaccuracy could have massive consequences if AI takes it as fact.

In terms of diagnostics, AI could return large-scale misdiagnoses, prescribe treatments incorrect, or process its own learnings incorrectly. Given the scale that AI works at, the cost of a single bad decision could have far-reaching consequences if left unchecked.

Ethical considerations

As AI becomes ever more integrated into our healthcare system, humanity must reckon with the ethical consequences this may have. For one thing, it is already well-documented that AI exhibits signs of racial and gender bias. But perhaps even more concerning is the fact that artificial intelligence is not capable of human empathy.

This could significantly impact diagnostics, as AI may comprehend a diagnosis medically but not grasp its psychological and emotional effects on the patient. We need to be cautious not to delegate too much of the diagnostic process to AI, risking the neglect of the vital patient- doctor relationship.

Adjusting to global changes

It’s important to recognize that the integration of AI in medical diagnostics signifies a fundamental change for the worldwide healthcare sector. There is a need for extensive preparation, including training, public awareness initiatives, and open communication between medical professionals and patients, to facilitate this major transition.

The effectiveness of AI integration should not be gauged solely by its ability to save time and reduce costs, but rather by its societal impact, the value it adds for individuals, and its level of societal acceptance.

Patients with type 1 diabetes who are receiving insulin treatment and may experience hypoglycemia must notify the National Driver Licence Service (NDLS) and follow the precautions outlined in the Medical Fitness to Drive Guidelines from April 2017. The purpose of this study was to evaluate both awareness and compliance with these guidelines, identify if certain demographics exhibit higher adherence rates, and determine if patients receive counseling from their general practitioners concerning safe driving practices.

In Ireland, the health of drivers is monitored through both European Union laws and regulations established by the Road Traffic Acts in Ireland. The Medical Fitness to Drive Guidelines represent an interpretation of these laws and have been developed based on current medical evidence and established international practices. They outline driving restrictions for various medical conditions, including insulin-treated diabetes.

In Ireland, individuals with type 1 diabetes make up 10-15% of the entire population of diabetes patients, totaling just over 207,000. There is ongoing debate over whether individuals with diabetes experience higher rates of accidents compared to the general public. Existing studies often do not differentiate between diabetes types and rely on patient recall, which indicates a need for high-quality, extensive prospective studies.

Prior research has indicated that healthcare professionals frequently provide insufficient guidance to patients with type 1 diabetes regarding safe driving. While there have been studies published internationally on this subject, significant data specifically from Ireland is lacking.

The primary safety issue for individuals with type 1 diabetes related to driving is hypoglycemia. Increased driving risks are associated with those who frequently endure severe hypoglycemic episodes, those who have previously experienced a hypoglycemic episode while driving, and those who do not check their blood glucose levels before getting behind the wheel.

It seems that patients often decide whether to drive based on their awareness of hypoglycemic symptoms. However, research has shown that relying on symptom-based estimates of blood glucose levels is neither accurate nor safe.

There are evident gaps in knowledge among both patients and healthcare professionals regarding the safe driving recommendations for individuals with type 1 diabetes. Enhanced access to information about reducing driving risks associated with diabetes is necessary for patients who use insulin to become more knowledgeable about driving regulations and recommendations.

Methods

A total of 107 participants were involved in our study, comprising 55 males and 52 females. The participants’ occupations included manual (6), professional (48), skilled workers (20), as well as unemployed (25) and retired (8) individuals. On average, patients in the study had been diagnosed with type 1 diabetes for 18.5 years.

We performed a cross-sectional, quantitative survey using a SurveyMonkey link to a self-created questionnaire. The questionnaires were distributed through diabetes clinics at CUH, GP surgeries, and online diabetes support groups.

Data was recorded in Microsoft Excel and analyzed using SPSS software. The chi-squared test was employed to determine P values for the strength of the association between different study variables. The Clinical Research Ethics Committee of the Cork Teaching Hospitals granted approval for the study.

Severe hypoglycemia while driving

In terms of severe hypoglycemia experienced during driving—defined as an episode requiring assistance from another person—one participant reported having a severe hypoglycemic episode while driving within the past year, and two participants reported two such episodes. One patient mentioned that a previous hypoglycemic episode while driving had led to an accident.

When suspecting hypoglycemia while driving, 11 (10.3%) participants planned to continue driving with heightened caution, 67 (62.6%) would stop driving, remove the keys from the ignition, relocate to the passenger seat, consume a carbohydrate source, and then resume driving, while 29 (27.1%) indicated that they would stop driving, take the keys out of the ignition, move to the passenger seat, eat/drink a carbohydrate source, and rest for at least 45 minutes before driving again.

Discussion

Most participants in this study were conscious of the fact that driving when blood glucose levels are below 5mmol/l is unsafe. This awareness is crucial, as cognitive impairment has been shown to occur when blood glucose drops below this threshold. However, the blood glucose testing practices among drivers with type 1 diabetes are largely inadequate, and a significant number of participants were not compliant with the established guidelines.

It is concerning that 8.4% of patients never keep their testing kit in their vehicle while driving, and only 34.6% consistently check their blood glucose before driving. Fourteen percent do not test their blood glucose before driving, and among the 36 individuals who seemed to understand the guidelines from the Licensing Authority, only 15 (41.7%) said they always monitor their blood glucose level before driving.

It is worth noting that there are no stipulations regarding regular blood glucose monitoring for obtaining a standard driving licence. Nevertheless, neglecting to check blood glucose levels may lead to legal repercussions, as earlier research has indicated, so effective education from healthcare professionals is crucial.

Only 29 (27.1%) of the participants understood the suitable management of hypoglycaemia while driving, indicating that a small percentage of patients in the study were informed.

The study focused on patients with type 1 diabetes for several reasons. From reviewing the literature related to diabetes and driving, it is evident that type one and type two diabetes patients largely represent distinct groups. For instance, individuals with type two diabetes are typically older and often have multiple comorbidities or significant complications from diabetes, such as retinopathy or neuropathy, which can also affect their driving safety.

However, a future study could include patients with type two diabetes undergoing insulin treatment or oral medications that carry a risk for hypoglycaemia, which would likely produce intriguing findings.

Strengths and limitations

A notable strength of the study is that the sample size is comparable to or even larger than other relevant studies in the field, most of which were published abroad, apart from a clinical audit conducted in Sligo Regional Hospital in 2013.

One limitation of the study is that participants were not asked if they had notified the NDLS about their diabetes, raising questions about adherence to legal requirements. Since the data was self-reported, there may have been some bias. Additionally, this study included responses from individuals who actively partake in diabetes support groups, which may suggest that these patients possess greater knowledge about driving regulations compared to individuals with type one diabetes in the broader population.

Conclusion

The risk of hypoglycaemia is a significant concern for individuals with type 1 diabetes. It is essential for health professionals to thoroughly review current driving practices and maximize opportunities to provide information and reinforce safety measures for patients, as outlined in the Medical Fitness to Drive Guidelines.

The clinical importance of this study is to enhance patient care through adequate education and to contribute to the safety of all drivers on the roads.

General practitioners often see patients with diabetes more regularly than other healthcare professionals who are involved in this care area. They must be well-versed in current driving guidelines and regulations for individuals with type 1 diabetes to provide the most accurate and updated information.

The ADA has cautioned against across-the-board driving restrictions, advocating instead for assessments on an individual basis.

The American Diabetes Association (ADA) asserts that having diabetes should not prevent someone from driving, emphasizing that only a medical professional should determine if complications are severe enough to restrict an individual from driving.

A new position statement published in the January issue of Diabetes Care advises against universal bans or restrictions. It suggests that patients facing potential driving risks due to their conditions be evaluated by their regular physician who treats individuals with diabetes.

“There have been inappropriate pressures to limit driving licenses for those with diabetes, and we were worried these recommendations were coming from individuals lacking sufficient knowledge about diabetes and were needlessly overly restrictive,” explained Dr. Daniel Lorber, chair of the writing group that created the position statement and director of endocrinology at New York Hospital Queens in New York City.

“The vast majority of individuals with diabetes drive safely,” noted Lorber. Currently, states have varying laws regarding driving and diabetes, and the ADA advocates for a standardized questionnaire to evaluate driving safety.

Nearly 19 million individuals in the United States have been diagnosed with diabetes, a condition that affects blood sugar levels. The primary concern regarding drivers with diabetes arises from the risk of low blood sugar (hypoglycemia), which may lead to confusion and disorientation. Although a hypoglycemic episode can impair driving ability, the ADA states that such occurrences are uncommon.

An analysis of 15 previous studies on the relationship between diabetes and driving revealed that, in general, people with diabetes have between a 12 percent and 19 percent increased likelihood of being involved in a motor vehicle accident compared to the general population of drivers.

However, society often accepts more dangerous driving situations. According to the ADA, a 16-year-old male faces a 42 times greater likelihood of being involved in a car accident compared to a woman aged 35 to 45. Individuals with attention-deficit hyperactivity disorder (ADHD) have an accident risk that is roughly four times higher than that of the general population, and those with sleep apnea are approximately 2.4 times more likely to be involved in a crash.

“The challenge lies in identifying individuals at high risk and creating measures to help them reduce their chances of driving accidents,” noted the ADA committee.

For instance, people with diabetes who use insulin are at high risk for experiencing hypoglycemia. The ADA advises those on insulin to check their blood sugar before operating a vehicle and to retest at regular intervals if their drive lasts longer than one hour.

“Nowadays, patients with type 1 diabetes are just like everyone else. There’s no justification for limiting their ability to drive,” stated Dr. Joel Zonszein, who leads the clinical diabetes center at Montefiore Medical Center in New York City. “Today’s patients are quite knowledgeable and have access to more technology to manage their diabetes and prevent hypoglycemia.”

For individuals at risk of severe hypoglycemia, the ADA recommends against starting a long drive with blood sugar levels that are low-normal (between 70 and 90 milligrams per deciliter) without consuming some carbohydrates to avoid a drop in blood sugar while driving. The ADA also suggests keeping a quick source of carbohydrates (such as fruit juice, hard candy, or dextrose tablets) in the car to swiftly raise blood sugar, along with having an additional snack like cheese crackers available.

Other diabetes-related factors that could impact driving include diabetic eye disease (retinopathy) and nerve damage (peripheral neuropathy). Retinopathy can impair vision, whereas neuropathy may limit the ability to feel the gas and brake pedals. If these complications are severe, driving could become problematic.

The ADA advises individuals with diabetes who might be a risk while driving to seek evaluation from a physician knowledgeable about diabetes. If their condition jeopardizes their ability to drive safely, doctors can inform state licensing agencies. The ADA does not advocate for mandatory physician reporting, as it could discourage individuals with diabetes from discussing these matters with their healthcare providers.

The key takeaway for those with diabetes, according to Lorber, is to “check your sugar before you drive, and do not drive if your levels are below 70 mg/dL.”

Due to the potential for a substantial decrease in glucose levels in the central nervous system (CNS), the functioning of higher brain centers diminishes, leading to a reduction in cerebral energy requirements.

Hypoglycemic conditions can be induced by medications or substances such as insulin, alcohol, or sulfonylureas. Less commonly, they can be caused by salicylates, propanolol, pentamidine, disopyramide, hypoglycin A, or quinine.

Non-drug-related hypoglycemia can arise from fasting, exercise, tumors, liver disease, severe nephropathy, or have an autoimmune basis.

Symptoms and signs can be adrenergic, presenting as sweating, anxiety, general tremors, palpitations, lightheadedness, and sometimes hunger.

Manifestations affecting the CNS may include confusion, inappropriate actions, visual disturbances, stupor, coma, and seizures.

In the early stages of hypoglycemia in drivers, perception, attention, and sensitivity to contrast in visual fields may be compromised. Additionally, cognitive decline is often linked with visual impairment.

Other symptoms that hinder driving include issues with directional control, lack of focus, drowsiness, fatigue, and prolonged reaction times.

When a diabetic driver begins to experience hypoglycemia symptoms, they have already suffered from impaired driving capabilities, posing an accident risk in certain traffic situations.

Many drivers experiencing hypoglycemia believe they are capable of driving safely; however, upon observation, they often exhibit poor judgment or extremely slow reactions.

Only when a driver with hypoglycemia experiences symptoms like tremors, lack of coordination, and visual disturbances do they decide to halt driving.

Thus, the primary concern for these drivers is cognitive impairment—usually unrecognized by them—that renders them unfit for driving and compromises overall safety.

If a hypoglycemic episode in an unconscious individual is not treated promptly, it may lead to seizures and a genuine deficit in brain energy, resulting in irreversible neurological damage or death.

Guidance for Managing Hypoglycemia

• The indications of hypoglycemia are more common in diabetic individuals while driving compared to other daily activities, which negatively impacts their ability to respond to unexpected situations on the road.
• Drivers with diabetes should be educated to recognize their hypoglycemia symptoms early and know the appropriate actions to take in each situation. Delaying response can increase the likelihood of accidents.
• Acute adrenergic symptoms typically lessen with the consumption of glucose or sucrose.
• When individuals on insulin suddenly experience confusion or act inappropriately, they are advised to consume a glass of juice or water mixed with three teaspoons of sugar.
• It is recommended that drivers keep sweets, candies, sugar cubes, or glucose tablets readily available in the vehicle.
• Most hypoglycemic episodes can be addressed through food containing glucose or sucrose for several hours.
• Nevertheless, for patients on sulfonylureas, hypoglycemia may recur for several days, so these individuals should be advised that even if symptoms improve after consuming glucose or sucrose, they must see a doctor immediately and refrain from driving.
• A hypoglycemic driver who continues to experience confusion and visual disturbances despite taking sugar should not drive and should seek assistance for urgent transport.
• A patient who exhibits CNS symptoms due to hypoglycemia and does not respond adequately to oral sugar needs to be taken to an emergency department for treatment.
• The indications of acute hypoglycemia combined with loss of consciousness prevent an individual from being fit to drive.
• A diabetic individual should not drive if their blood glucose levels drop to dangerously low levels. The doctor will inform them of the recommended blood glucose thresholds pertinent to their individual case.
• The diabetic driver should understand that if they notice a decline in focus, they should immediately pull over and consume carbohydrates.
• They may resume driving only once they fully recover, always ensuring to check 1-2 hours later that their blood glucose levels have not decreased again to unsafe levels.
• Moreover, the recovery time from hypoglycemia to being able to drive safely will vary depending on the trip type, road conditions, and whether they have company in the vehicle.
• Before embarking on a journey, the patient should always check their blood glucose levels, making sure they are within the normal range established by their doctor.
• During trips, meal schedules and medication regimens should be adhered to. It is advisable for the driver to keep sweets, sugar cubes, or glucose tablets in the car.
• Throughout journeys, they should be accompanied by individuals who are familiar with their condition and can provide assistance in case of complications. They should take breaks every hour.
• The driver should keep a visible medical report inside the car that specifies their condition and treatment so that it can be identified and appropriately managed in the event of an accident.
• Drivers should refrain from consuming alcohol prior to driving. Diabetic drivers, in particular, are advised against drinking alcohol due to its potential interference with their medication, thereby increasing risks associated with driving.