The industrial revolution is a concept that encompasses technological innovations, particularly in the fields of automation, control systems, information technology, and collaboration between humans and advanced technologies, one of which is artificial intelligence (AI). The health sector is one of the fields most affected by technological advances, leading to digitalization and a transformation of healthcare services, including new methods of care, diagnosis, and monitoring of patients’ health status (Melo & Araujo, 2020). Healthcare systems around the world are facing growing challenges in achieving their care objectives, including an aging population, an increasing burden of chronic diseases, and the rising cost of healthcare on a global scale. The rapid technological progress in the current era of the industrial revolution should serve as a factor in solving health problems, making healthcare more efficient and contributing to improved public health quality. Furthermore, the high occurrence of negligence in medical services can result in a partiality of trust lost on health care professionals (Pramita & Wibawa, 2022).
AI is a recent technological revolution and emerged as the task to be capable of processing vast number of hard-to-process data fast and accurately. Artificial intelligence (AI)–enabled algorithms are becoming ubiquitous to aid in diagnostics, treatment planning, and patient management. A study by Miranda et al. demonstrated that AI can use large data to forecast disease outbreaks, making it useful as an early-warning system for disease surveillance (Miranda et al, 2025). Integration of AI with the Internet of Things also supports real-time health monitoring, more precise medical diagnosis and more accurate treatments leading to increase efficiency and effectiveness for healthcare services (Melo & Araujo, 2020). On a larger scale, AI’s deep learning capabilities allow researchers to analyze huge amounts of information and develop clinically actionable findings that aid diagnostic reasoning as well as the timely and accurate decision making in patient care.
However, despite these for advantages, the use of AI in healthcare premises also poses multiple challenges that should not be overlooked. The problems of bias in algorithms, while well known and widely studied, have not abated. The gap between the potential benefits and the risks accompanying the integration of AI into healthcare systems highlights the need to critically assess the role of AI in clinical practice. Therefore, this essay is entitled “The Benefits and Drawbacks of AI-Assisted Healthcare in Clinical Practice”, aiming to examine and provide a more comprehensive understanding of how AI functions within global healthcare services.
DISCUSSION
One of the AI subfields that is frequently developed in clinical practice is machine learning, which has the capacity to process large volumes of clinical data. Its algorithms can identify patterns and predict medical outcomes with a high degree of accuracy. These capabilities enable more accurate disease diagnosis, patient-specific management strategies, and the detection of subtle changes in patients’ vital signs that may indicate emerging health problems.
A study on the development of AI in the healthcare field conducted by Esteva et al., entitled “Dermatologist-level classification of skin cancer with deep neural networks,” examined the use of AI in skin cancer classification and demonstrated performance comparable to that of dermatologists. The study also integrated AI with mobile devices, which expanded the scope of clinical practice and helped accelerate clinical decision-making by dermatologists (Esteva, 2017). Beyond clinical benefits, the adoption of AI in healthcare may also have economic and social impacts by offering several advantages, such as reducing post-treatment costs through the use of AI to analyze post-care outcome patterns and identify the most effective solutions based on patient profiles. This personalized approach can significantly reduce costs associated with potential post-treatment complications. This is supported by a study by Nguyen et al., which reported that post-treatment complications are one of the main factors contributing to increased healthcare costs, but that the integration of technologies such as AI can reduce these costs by minimizing the occurrence of such complications (Nguyen et al, 2019). In addition, AI has the potential to empower individuals to manage their own health through its integration into wearable digital devices, such as smartwatches and mobile applications. This integration enables the continuous collection of users’ health data anytime and anywhere and provides personalized lifestyle recommendations and health alerts, thereby contributing to a healthier population and reducing overall healthcare expenditures.
Based on previous studies on the benefits of AI integration in healthcare systems, valuable insights have been provided regarding the highly promising role of AI in improving diagnostic accuracy, supporting informed decision-making, and enabling optimal care planning, thereby potentially reducing medical errors and enhancing patients’ quality of life. However, alongside these promising benefits, the adoption of AI in healthcare also presents several drawbacks, particularly ethical challenges that require careful consideration. The areas of accountability, transparency, consent, and privacy are also important to address when it comes to the applications of intelligent machines like AI in healthcare decision making. Issues related to responsibility and accountability may arise as AI takes on a greater role in clinical decision making, raising urgent questions about liability, such as who should be held responsible if an AI-generated recommendation causes harm to a patient. It’s not just the impact of new technologies like AI in healthcare that could breed scepticism, both around privacy crime committed from mass data profiling and from where AI might process this type of data linking it to personal data. This may be felt as an intrusion of privacy to Patients, especially if their personal information is shared to third parties such as the AI systems.
A study by Shaheen highlighted that inequality is one of the bias-related risks that may arise in AI-based healthcare, particularly when certain ethnic groups or genders are underrepresented in the data used to train AI systems, potentially leading to inaccurate and unrepresentative outcomes. Such non-representative data can increase the occurrence of inequities within healthcare systems (Shaheen, 2021). This concern is further supported by studies by Davenport and Dreyer, which reported that the integration of AI into healthcare systems is vulnerable to algorithmic bias, resulting in predictions based on non-causal factors such as patients’ gender or ethnicity (Davenport & Dreyer, 2018). In addition, there are significant challenges that require attention, particularly regarding the implementation of AI being constrained by the quality of electronic health data in current healthcare practice. Medical data are often unstructured, making them unsuitable for AI algorithms. Beyond data structure issues, healthcare institutions across different countries may use varying data formats and quality standards, necessitating manual revision, quality control, and relabeling of existing data before AI can be effectively implemented on a global scale. These processes pose substantial barriers to AI implementation due to the considerable time and costs required to carry them out.
AI could shape the future of global health, that is from what we have discussed before, but rigorous regulations has to be put in place ensuring safe use particularly applying AI technology to healthcare systems. Moreover, the staff members are still necessary for more cognitively and emotionally complex tasks. Therefore, integrating AI into healthcare systems should not eliminate the role of doctors, but rather utilize expertise that computers cannot replicate. Therefore, the integration of AI and doctors is important. This approach was examined in a five-year qualitative study conducted to investigate the adoption of AI in stroke care. The findings showed that AI adoption resulted in a relatively effective approach to healthcare delivery for stroke patients, in which AI provided recommendations related to patient diagnosis and subsequent management, while physicians assessed their validity through several verification steps. Through this approach, the study reported a significant improvement in diagnostic and treatment success, while clinical accuracy and human oversight were maintained (D’Adderio & Bates, 2025). This view is reinforced by a study by Bajwa et al., which highlights that AI is intended to augment, rather than replace, human intelligence. A key consideration in integrating AI into healthcare systems is ensuring that essential elements of human interaction in medicine are not displaced, but instead supported in ways that improve the efficiency and effectiveness of those interactions (Bajwa et al, 2021).
CONCLUSION
AI technology used in health care service is an innovation and has the power to bring a tremendous change for global health services. One of the greatest potential benefits of AI in healthcare is its capacity to quickly process vast quantities of medical data, empowering doctors to find diseases, patient risks and population-level health trends. As a whole, AI is a transformational technology for health with advantages we can broadly classify into the following tangible achievements: (1) supporting early prediction of various risks and diseases; (2) assisting in disease prevention and control; (3) enabling data-driven decision-making; (4) improving the efficiency of healthcare costs; and (5) contributing to patient empowerment. At the same time, its risks and limitations include: (1) potential harm to patients in the event of AI errors; (2) the risk of bias and inequity in healthcare delivery; (3) issues related to privacy and security; and (4) barriers to implementation in current healthcare services due to variations in data quality across healthcare facilities. Successful integration of AI into healthcare systems must complement human expertise, as the impact of AI on healthcare is evolutionary rather than revolutionary. Accordingly, collaboration among stakeholders, standardization, and strong policy frameworks are essential to fully realize the potential of AI while upholding patient-centered care.
By: Nohan Noer Adnan
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