Can I get my UTI Diagnosed, Investigated and Treated via AI?

According to NITI Aayog, Government of India, “Artificial Intelligence (AI) refers to the ability of machines to perform cognitive tasks like thinking, perceiving, learning, problem solving and decision making.” It is a General-Purpose Technology (GPT) that has been programmed to emulate human intellect in machines, especially computer systems that possess human-like thoughts and behavioral patterns, thus giving it the power to unlock concealed human capabilities further facilitating the introduction of more prompt, precise, and efficient solutions by recommending more effective contributions to society. The current classification system categorizes AI into four primary types: reactive, limited memory, theory of mind, and self-aware.

Urinary Tract Infection, often abbreviated as UTI, is a bacterial or fungal infection in the urinary system which is formed by the kidneys, ureters, bladder and the urethra. The incidence of UTI is known to be more predominant among females than males, owing to the female anatomy that delineates shorter urethral length, external urethral meatus lined by mucosa, thinner and more sensitive skin around the vagina, and close proximity to the rectum. All of this form a conducive environment for the organism to colonize the host urethral epithelial cells and gain ground in causing bladder infections or cystitis, traversing further to cause ascending infections that lead to pyelonephritis – where one or both kidneys become infected. Whereas in males, abnormalities of the urinary tract that are physiological are implicated in the occurrence of UTI. The most common organism responsible for causing uncomplicated and complicated UTIs is Escherichia coli or E. coli; the specific variant being Entero Pathogenic E. coli or EPEC. The second most common being Klebsiella pneumoniae.

When a patient presents with the classic symptoms of burning and painful micturition, with urge and overflow incontinence, fever and pain in the lower abdomen; the suspicion of UTI arises. Various factors such as failure of prior treatment modalities, repeat infections resulting in morbidity and mortality with a poor outcome hold the capacity to complicate the infection. Therefore, it is necessary for the clinician to advise a diagnostic workup of a midstream clean catch urine sample or catheterized (indwelling or straight in-out) urine sample, the latter being an invasive procedure. These samples are collected before initial antibiotic administration to improve the accuracy of treatment by determining if the UTI can be resolved by first line medications or if additional treatment modalities need to be exercised. This being imperative, still does not rule out the urgency to start immediate treatment in cases that have a strong clinical presentation. The urine samples collected are sent for a culture test whose results take 1-3 days to be declared. These culture tests identify the growth of specific bacterial or fungal organisms in the urine along with their respective Antibiotic Sensitivity testing which aids the healthcare providers to select the appropriate antibiotics for battling the infection.

While there are cases of false-positive and false-negative test results, studies have proved that using AI generated algorithms these can be re-categorized to true-negative and true-positive results in comparison to documented diagnosis. AI methods employed for UTI diagnosis are ANN (Artificial Neural Network), SVM (Support Vector Machines), KNN (K-Nearest Neighbors); out of which ANN is known to be highly reliable upon as it is applied on a large scale, therefore maintaining a balance between accuracy and efficiency. All these methods are based upon retrospective data findings which involve the patient details, gender, clinical predicament, signs and symptoms, laboratory findings – biochemical urinary markers, gross urine appearance and microorganisms associated with the causation of UTI. AI-based investigations have fulfilled all screening test requirements and have demonstrated a preference for high sensitivity and diagnoses, aiding clinicians to be more accurate, precise, consistent and faster with their diagnoses. Additionally, it can be leveraged for remote patient monitoring, which gives clinicians immediate feedback regarding a patient’s health or progress throughout treatment and permits them to modify a patient’s course of therapy as warranted.

To further increase precision, it can additionally predict which medications would work most effectively in particular patients based on their individual DNA and medical records, showing a promise to curb antimicrobial resistance. Machine learning algorithms that power AI in identifying crucial elements for clinical diagnosis of suspected UTI are significant given the rising requirement driven by the elderly population in a great deal of developing and industrialized nations. Thus, a major overhaul is required to maximize capacity, boost cost efficiency and affordability in testing centers.

Furthermore, hospitals may more reliably anticipate their equipment requests and allocate resources accordingly by embracing predictive analytics. With AI, there is possible reduction in workload, reduction in expenditure on culture agar acquisition, time cost, reduction of added expenditure on pursuing bacterial culture – all these, that too, on a national scale. This technology also could assist in the automation of numerous time-consuming tasks inside the healthcare system that would otherwise require significant manual effort from staff members, such as managing clinical data, scheduling appointments, providing telehealth services, and maintaining medical records/documentation. Ultimately, this can lower payroll expenses and increase overall efficiency.

The pros being stated, there are also potential limitations to AI based investigations, diagnosis and treatment. Firstly, in government funded hospitals, which run busy with surplus patient load, clinicians need to prioritize their finite time leading to relatively lacking clinical detailing. Secondly, since AI is built around the retrospective study genre to analyze and dispatch results, it makes it rather tough to elucidate some of the specifics, such as possible sample mislabeling due to random individual error. It is also important to keep in mind that the outcome AI examines is a cultural predictability, not a clinical or therapeutic one. It might not always yield accurate results as particular data sets or systems might feature integrated biases. And when a clinician would rely on these results, they might end up making errors in diagnosis and faulty prescriptions.

To avoid this, it is vital for businesses employing AI technologies to regularly inspect their algorithms for prejudice and issues with precision over time. It is imperative to promptly and effectively resolve any potential errors in order to prevent collateral damage or inconvenience to the business, its patient base, or both. Another important factor to take into consideration would be the added value AI would provide to an organization. It is meaningless for a small clinic or set-up to employ the use of AI as it would take a fiscal toll on them due to huge investment requirements for hardware infrastructure and personnel training. Hence, it is necessary for such organizations to weigh their available options carefully before arriving at a decision.

After meticulous execution and incorporation into pre-existing organizational procedures by trained engineers for its smooth implementation, AI holds the potential to improve global user experience, lower operating cost, improve organizational effectiveness and better overall health outcomes. With the perpetual onrush of AI in healthcare tech, it is a launch pad for revolutionizing healthcare. And these pros clearly outweigh its cons.