Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives

Abstract

Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.

Keywords: artificial intelligence; computational models; forecasting; future; medicine; predictive modeling.

Figure 1

Number of PubMed indexed publications on AI in medicine.

Figure 2

The use of neural networks for medical decision support. This procedure typically includes multiple stages such as gathering and preprocessing data, developing the model, refining the model via cross-validation, selecting the optimal model, and amalgamating it with systems like desktop software or embedded hardware and devices.

Figure 3

Neural elements of a multilayer feedforward backpropagation network.

Figure 4

Trends in the number of PubMed-indexed publications on AI in medical imaging.

Figure 5

Public opinion on AI and whether it will lead to better/worse/similar outcomes for patients. (Source: Pew Research Center [146]). Note: Respondents who did not give an answer are not shown.