Introduction:
AI or artificial intelligence in a way, resembles the structure and operating mechanisms of a human brain. AI involves two forms of application namely physical and virtual. One arena wherein the potential of AI is being increasingly explored is endocrinology and hormone management. Endocrinology is a branch of medical science that focuses on the hormone-producing glands of the human body like the pancreas, thyroid, and adrenal glands. With AI creating a revolution, the future of endocrinology looks good and promising and allows possibilities for more precise diagnoses, customized treatments, and enhanced patient outcomes.
What Are the Two Forms of Application in AI?
The following are the two forms of application:
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Physical.
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Virtual.
The physical component mainly involves robots. The term ‘robot’ is derived from the Czech word ‘robota’ which refers to ‘forced labor’. The physical robotic forms were conceptualized by great inventors like Leonardo Da Vinci and this component has found widespread use in the field of endocrinology like robot-assisted surgery of prostate or adrenal cancer.
Virtual forms of AI are used in electronic medical records (EMR) wherein specific algorithms are utilized to identify patients and gather health data.
What Is Machine Learning in AI?
Machine learning (ML) is a field of AI that encompasses the development of models and fine works that allow computer systems to enhance their performance on a particular task progressively. The ML algorithms can be supervised (algorithms that are built based on previous examples), unsupervised (spontaneous detection of pattern), or reinforcement learning (utilizing reward or punishment techniques to derive the needed result).
The combination of artificial intelligence and machine learning technology finds its use in the field of medicine at three levels which are as follows:
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Physicians: This technology enables health care professionals to enhance diagnostic accuracy and assists them with suitable therapeutic and surgical interventions.
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Health Systems: The workflow gets improved and there are reduced errors.
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Patients: Customized diagnostic and therapeutic modalities can be obtained depending on the unique genetic and phenotypic features of each patient.
What Is the Role of AI in Endocrinology?
The role of AI in endocrinology is as follows:
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AI can detect the progression of certain endocrine disorders which aids in the proactive management of these endocrine conditions. For example, AI can assess the insulin levels of the patient, exercise, diet, and other lifestyle factors to predict the possibility of one developing diabetes. This predictive ability permits physicians to formulate preventive strategies thereby lowering the burden of the disease both on the patients and healthcare systems.
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With regard to treatment, AI has the ability to revolutionize hormone management. In the current era, hormone therapy is a huge trial-and-error process and physicians adjust the dosage of medications based on the patient’s feedback. The role of AI in this aspect is that it assesses the patient’s data and detects the suitable dosage and timing of hormone therapy. This aids in lowering the side effects and helps in increasing the efficacy. This customized approach to hormone management can efficiently improve patient outcomes.
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Patient monitoring which is a critical component in endocrinology is facilitated through AI. It is necessary to monitor the hormonal levels from time to time to provide an effective treatment. The usual procedure of ensuring this is by frequent hospital visits which can be difficult and time-consuming for the patients. With the advent of AI-powered wearable devices, one can monitor their hormonal levels in real-time which alerts physicians in case of any abnormalities. This is not only convenient for the patient but also permits timely intervention which helps prevent complications.
What Is the Role of AI in Diabetes?
The following are a few roles of AI in diabetes:
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AI acts as a personal guide to those with diabetes by detecting which patterns in behavior result in low or high blood sugar levels. If there is a precise data feed for food consumption and activity, then AI can identify changes and suggest actions to enable individuals to respond early.
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Since AI can process numerous data, AI identifies the connections between behavior, treatment, and response to formulate customized recommendations for diabetic individuals at a cost and scale that was not possible previously.
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A system has been developed by physicians using AI that enables them to identify patients who are at risk of developing type 2 diabetes three, five, or even 15 years from now. This technology works based on patterns in patients like height, weight, gender, smoking and drinking history, and blood sugar levels. Early methods of detection are based on both genetic and behavioral patterns in individuals.
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Currently, AI is being used in developing an automated insulin delivery system with the use of an artificial pancreas. The system observes and obtains information from the user’s response to exercise, food, and other factors and then adjusts the dosage of insulin accordingly.
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With all these beneficial aspects of AI, it does not mean that AI alone can be used to treat diabetes but rather, AI provides effective and economical solutions to prevent and manage diabetes.
How Does AI Play a Role in Reproduction?
The role of AI in reproduction is as follows:
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There are many trials going on in reproductive endocrinology regarding the application of AI to predict the fertility of women, to identify the quality of blastocysts and oocytes, along with decision-making algorithms for ovarian stimulation.
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With regard to male reproduction, there are a few studies that focus on the examination of seminal fluid. There are several trials that utilize image recognition systems for morphological classification of sperm, to determine its motility, and to select gametes with minimal DNA damage for assisted reproduction technique (ART). Though these are effective, these tools have not yet been implemented in daily practice and hence cannot be put into use in the general population.
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There are many uses of AI in male fertility, especially in analyzing the characteristics of sperm, and so on, but the usage of AI should focus on providing tools to enhance diagnostic accuracy and in predicting the possible outcomes of treatment.
How Does AI Help in Diagnosing and Treating Thyroid Disorders?
The following are some of the roles of AI in diagnosing and treating thyroid disorders:
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Thyroid ultrasonography (TUS) is a diagnostic procedure that is done to visualize the thyroid gland and helps in the detection of thyroid nodules. However, there are various challenges that are faced in the care of patients with thyroid nodules like uncertainty in the cytopathologic diagnosis, inconsistent evaluation by the ultrasonographer, inaccurate prognosis, and so on.
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Fine needle aspiration (FNA) is one of the preferred preoperative examinations in the assessment of thyroid nodules. Recent studies show that genetic and morphological classifications supported by AI are quite precise in differentiating benign (noncancerous) from malignant (cancerous) thyroid nodules.
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Also, it has been found that the use of AI as a second opinion minimized the number of FNA procedures by 27 percent.
What Are the Challenges of Using AI?
Though the application of AI in various medical fields like endocrinology is immense, there are certain challenges. One such challenge is data privacy which is a major concern since AI algorithms need access to sensitive patient content. Maintaining this security can be challenging. Also, there are robust regulatory frameworks to invigilate the use of AI in healthcare to ensure its ethical use and safety.
Also, the successful use of AI also depends on the upskilling of healthcare professionals. Physicians should be sufficiently trained to interpret AI-generated results and make informed decisions. Also, interdisciplinary collaboration between technologists and endocrinologists may be required.
Conclusion:
Artificial intelligence or AI has a good future in the field of endocrinology and hormone management. Every aspect of endocrine care can be enhanced using AI. However, further research is mandatory to enhance the accuracy and generalisability of AI-based models. Hence, relying on AI alone for diagnosis in clinical practice should be avoided. AI also has a few challenges with respect to data privacy, workforce development, and regulation. With further efforts to resolve these challenges, AI can create a revolution in endocrinology and can help render better care for patients and improve the efficacy of treatment.
