Background
Worldwide technological development has led medical professionals to focus on improving clinical services through information technology. Artificial intelligence (AI) can simulate human thinking and help machines solve problems. AI is widely used in medicine to improve treatments, care for chronic diseases, suggest precise therapies, and reduce errors. There are two common types of AI. The first type is expert systems, which are predictive computer systems that can make decisions like humans. They consist of a knowledge base and an inference engine. The knowledge base holds experience, while the inference engine updates it with new knowledge. Expert systems create clear information, are easy to maintain, and allow fast prototyping. However, they need to improve in acquiring new knowledge and enhancing performance. The second type is machine learning, which is the core of AI and helps computers become intelligent. Machine learning requires large data sets for training to improve performance. It focuses on reducing the number of parameters to improve efficiency, though this might reduce accuracy. The goal is to outperform humans in complex tasks through self-learning.
Introduction
Artificial intelligence (AI) is revolutionizing our world and is poised to become a fundamental tool in diverse fields, including healthcare, engineering, politics, and social affairs. In healthcare, AI's impact is groundbreaking, offering advancements in decision-making, diagnostics, patient interactions, record-keeping, and treatment. More importantly, scientists show that AI, especially through machine learning, can have diagnostic performance comparable to human experts and provide patient-centered care. According to Parliament’s Think Tank (2020), by 2035 AI will help increase labor productivity by roughly 37%. Despite these benefits, AI poses threats to the healthcare sector, particularly concerning data security and privacy, which can compromise the confidentiality of medical records. This threat can lead to algorithmic activation, errors, and system dysfunctions. This report discusses the positive impacts of AI on healthcare and the associated threats, acknowledging the novelty of the topic and the limited literature on AI's effects on health systems.
Utilizing AI
The health sector is sensitive and ethical specifically in recording the data and having sensitive information according to person. Integrating AI in the health system helps enhance diagnostic performance which can be comparable to the medical experts. Medical experts are the leading ones in the integration, however, AI can be more accurate and precise in the detection of computer-based results. Image recognition is one of the areas in which AI is highly advanced. For example, in a 2019 meta-study, they found that AI performs better than dermatologists for applications like melanoma detection. In the process of optical coherence tomography (OCT), a noninvasive imaging method, dermoscopy studies measured accuracy between AI and dermatologists. The study showed that by using those techniques and having AI integrated and used into the system the detection of melanoma is more accurate and precise. They have shown the result and compared it with the same result with dermatologists. The performance of AI-based techniques with dermatologists was reported superior or nearly the same as AI-based techniques in improving the detection of melanoma. In the same case, the AI-based algorithms achieved a higher ROC (>80%). The average sensitivity of these algorithms is 83.01%, and the average specificity is 85.5%. ROC (Receiver Operating Characteristic) is a measure used to evaluate the performance of diagnostic tests, a higher ROC value indicates better performance in distinguishing between patients with and without melanoma. OCT device compared to a non-AI integrated machine which is RCM (Reflectance Confocal Microscopy), is another non-invasive imaging technique that provides high-resolution images of the skin at a cellular level, studies gave an average accuracy of 82.72%. AI-based techniques have strong potential to improve the diagnosis and early detection of melanoma, leading to better patient outcomes. More research is needed to see how well these techniques work for different populations and skin types, improve image processing standards, and compare AI performance with board-certified dermatologists for practical use in clinics. In addition, this can help optimize patient care services, according to a study published in PubMed, specifically with acute chronic diseases such as acute appendicitis, and reduce medical errors to enhance the perfection of the reported results. According to a study at the National Institute of Health (NIH), in hospitals, nearly 10% of diagnostic errors cause adverse events occurring. Reducing diagnostic errors and medical errors is alerting and using AI-integrated machines is highly recommended because it can reduce the cognitive burden and having more algorithms enhance image detection.
Concerns about AI
While AI offers several benefits, there are significant concerns about its integration into the healthcare system. One major threat is the risk to data privacy and security, potentially compromising the confidentiality of medical records. AI algorithms processing recorded data may accidentally expose sensitive cases and details, leading to a loss of privacy. Moreover, AI can rapidly process or delete data, which poses a threat to data security. This is particularly concerning in hospitals, where medical records are crucial for informed decision-making. In the USA, the use and disclosure of Protected Health Information (PHI) must comply with the Health Insurance Portability and Accountability Act (HIPAA), which mandates strict confidentiality of health data. Multiple security requirements must be met to build trust in healthcare cloud computing and ensure the protection and privacy of medical information.
Additionally, AI systems programmed by humans can incorporate biases and discrimination, resulting in biased outputs. For instance, a study in a US hospital found that an AI-driven pulse oximeter overestimated blood oxygen levels in patients with darker skin, leading to their undertreatment. Many studies and research are needed to obtain accurate information regarding AI integration and outcomes. While AI is a new field for many sectors, understanding its advantages and disadvantages is crucial.
Conclusion
In conclusion, while AI presents remarkable opportunities to revolutionize healthcare by enhancing diagnostic accuracy, optimizing patient care, and reducing medical errors, it also brings significant ethical and security challenges. The potential for AI to outperform human experts in certain diagnostic tasks, such as melanoma detection, underscores its value in clinical settings. However, concerns about data privacy, security, and algorithmic biases highlight the need for strict regulatory frameworks and ongoing research. Balancing the benefits and risks of AI integration is crucial to ensuring that advancements in healthcare technology are both effective and ethically sound. As the field continues to evolve, a collaborative approach involving healthcare professionals, AI experts, and policymakers will be essential to harness the full potential of AI while safeguarding patient rights and privacy.
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