Artificial intelligence
Exploring the Application of Artificial Intelligence Techniques in Modern Medicine: A Review of Diagnostic and Prognostic Tools
1.0 Background of the Study
Artificial Intelligence (AI) is defined as a discipline within science and engineering that focuses on the computational understanding of behaviours typically regarded as intelligent, and the development of artefacts that exhibit such behaviours. The origins of AI can be traced back to Aristotle’s formalisation of logical reasoning through syllogisms. Inspired by early work in cognitive science, modern AI evolved into the development of computer programs designed to simulate intelligent human functions.
One of the pivotal figures in AI history, British mathematician Alan Turing, proposed that machines could exhibit intelligent behaviour if they could achieve human-level performance in cognitive tasks, a concept now famously known as the “Turing Test.” Since the mid-20th century, researchers have investigated AI’s utility across various domains, including medicine. In 1976, Gunn pioneered the application of AI in surgery by analysing acute abdominal pain via computer-assisted diagnosis.
Contemporary medicine is challenged by the need to collect, process, and interpret vast quantities of complex data for decision-making. AI systems have been developed to assist clinicians in diagnosis, treatment planning, and outcome prediction. Notable systems include Artificial Neural Networks (ANNs), fuzzy expert systems, evolutionary computation methods such as Genetic Algorithms, and hybrid intelligent systems.
Artificial Neural Networks (ANNs) are inspired by the biological nervous system and consist of interconnected nodes or “neurons” that process data in layers. Their capability to learn from historical data, analyse non-linear patterns, and handle imprecise information has made them highly effective for clinical applications, ranging from myocardial infarction diagnosis to image interpretation in radiology and histopathology.
Fuzzy Expert Systems handle the ambiguity inherent in medical data. By employing “fuzzy” rules rather than binary logic, these systems model reasoning with varying degrees of truth. Fuzzy logic has been used in diagnosing various cancers, interpreting imaging data, and even controlling anaesthetic delivery during surgery.
Genetic Algorithms, a form of evolutionary computation, mimic natural selection to solve complex optimisation problems. These have been applied in predicting treatment outcomes, identifying high-risk cancer patients, and enhancing diagnostic imaging.
Hybrid Intelligent Systems combine the strengths of individual AI techniques to improve accuracy and adaptability in medical applications. These systems have demonstrated potential in diagnosing breast cancer, assessing coronary artery conditions, and managing anaesthesia depth.
Despite significant advancements, the adoption of AI in clinical practice remains limited due to scepticism among clinicians and the need for more robust evidence. As such, further clinical validation through randomised controlled trials is essential.
2.0 Objectives of the Study
The study seeks to achieve the following objectives:
To examine the historical development and foundational concepts of Artificial Intelligence with relevance to medicine.
To explore the structure, function, and medical applications of Artificial Neural Networks in diagnostic and prognostic tasks.
To evaluate the utility of fuzzy logic systems in interpreting ambiguous clinical data and guiding medical decision-making.
To analyse the application of Genetic Algorithms in medical diagnostics, prognosis, and image processing.
To investigate the potential of hybrid intelligent systems in enhancing clinical outcomes through combined AI techniques.
To identify barriers to adoption and propose strategies for integrating AI technologies into mainstream clinical practice.
