Artificial Intelligence for Clinical Decision Support: Enhancing Diagnostic Efficiency and Patient Care in Resource-Limited Settings

Abstract

Artificial intelligence (AI) is a novel tool that has transformed the medical sector and, by far, it has made an impact, at least in regards to assisting clinical decision-making. Clinical Decision Support Systems (CDSS) with the help of artificial intelligence can positively influence the fact that diagnoses, treatment planning, and patient care optimization will be more accurate. One of the challenges experienced in healthcare system in resource constrained health care settings particularly in developing countries is shortages of trained medical professionals, absence of diagnostic facilities as well as access to specialized medical knowledge. The difficulties encountered usually lead to late diagnosis, misdiagnosis and patient outcomes. The use of artificial intelligence in clinical decision support systems can overcome these limitations and is therefore likely to improve the provision of healthcare.The paper considers the concept of artificial intelligence and how it can be applied to the clinical decision support system and enhance the efficiency of the diagnostic process and patient care within a limited resource setting of a healthcare center. Clinical decision support systems using AI are machine learning algorithms, natural language processing, and predictive analytics to analyze patient data, medical data and clinical guidelines to provide evidence-based advice to healthcare practitioners. These systems can assist the physicians in diagnosing the disease, and also in determining a treatment option and forecasting potential health risks.AI-based decision support tools can significantly decrease disease monitoring and diagnostic error cases, as well as improve the speed and quality of medical decision-making. The application of AI technologies also enables health experts to find patterns that can be associated with a specific disease and diagnose it during an early stage and propose appropriate intervention by processing large volumes of clinical data. The AI-based decision support systems may become an effective assistance in the resource-limited setting that employs healthcare workers who may not have access to expert knowledge and fill the void of clinical knowledge and better patient care.In spite of a brilliant future lie of AI in clinical decision support, it has several barriers to usage in the resource-limited environment. They include ineffective digital infrastructure, lack of standardized electronic health records, the issue of data privacy and the ethical factor, and the deficit of technical expertise in order to deploy healthcare systems based on AI.The paper proposes that the use of artificial intelligence-driven clinical decision support systems can lead to a radical change in the efficiency of the diagnostic process and the care providers within the environment of a medical facility with inadequate resources. However, in order to have successful implementation, investments in digital health, workforce, regulatory frameworks, and ethical governance are relevant to enable responsible and efficient application of AI technologies in healthcare.