AI in Healthcare

Healthcare stands as one of the most vital and dynamic sectors of modern society, encompassing a wide array of services dedicated to maintaining and improving human health. From primary care and disease prevention to specialized treatments and mental health support, ‘’Health systems worldwide are at a crossroads and face exponential healthcare cost developments that have far outpaced GDP growth rates to support health system sustainability’’ (Kuwati et al., 2023) healthcare systems worldwide strive to address the diverse and evolving needs of populations. This research review will discuss about ai in healthcare and ai-assisted surgery, and its role on enhancing diagnostics and medical efficiency and finally move on to the mistakes and accidents of ai, finishing with an evaluation of some of the sources used.

The integration of AI into healthcare is transforming the industry in numerous ways, enhancing service quality, efficiency, and accessibility of healthcare services. The main ways are. Enhanced patient engagement which is ai chatbots providing 24/7 assistance for appointment scheduling and empowering patients with immediate support. These chatbots can handle multiple inquiries simultaneously, ensuring timely responses without the need for human intervention. This accessibility is particularly beneficial for patients with chronic conditions requiring ongoing management. These chatbots can handle multiple inquiries simultaneously, ensuring timely responses without the need for human intervention. This accessibility is particularly beneficial for patients with chronic conditions requiring ongoing management. Second being personalized treatment plans through machine learning models that analyze patient data, including medical history, symptoms, and genetic information. These models predict disease risks, such as diabetes or heart disease, allowing for timely preventive measures and tailored treatment plans. This approach enhances treatment effectiveness and patient satisfaction while reducing adverse reactions and complications. Akbar & Muash (2025) argue that virtual assistants which are tools like the NHS help patients assess symptoms and guide them to appropriate care. AI is significantly improving in patient engagements (Zeb et al., 2024). These tools can also remind patients to take medications and monitor symptoms, leading to better health outcomes and reduced healthcare utilization costs.

AI-assisted surgery is revolutionizing the field of medicine by combining advanced robotic systems with artificial intelligence to enhance surgical precision and outcomes. The primary applications include complex procedures such as cardiac, neurosurgical, and orthopedic surgeries, where AI provides unparalleled accuracy. For example, AI can assist in coronary artery bypass grafting and heart valve replacement, providing precise control and reducing complications. In neurosurgery, AI helps navigate brain structures for accurate tumor removal, minimizing damage to healthy tissue. Orthopedic surgeries benefit from AI's precise implant positioning, improving long-term outcomes. AI can detect minor patterns between genetic codes that doctors would completely overlook (Shaheen, 2021). The techniques driving AI-assisted surgery involve robotic surgical systems (RAS), which allow surgeons to operate with enhanced precision through a console controlling robotic arms equipped with high-definition 3D vision systems (Scientia Educare, 2025). The benefits of AI-assisted surgery are substantial, with enhanced precision reducing errors and improved visualization through high-definition imagery allowing for better identification of anatomical structures. AI assistance can shorten surgical time and improve overall safety by monitoring intraoperative data and alerting surgeons to potential issues.

The integration of AI into healthcare diagnostics and operations is driving significant advancements in efficiency and accuracy. Zeb et al. (2024) offers the same claim. AI algorithms excel in medical imaging analysis, processing X-rays, MRIs, and CT scans to detect abnormalities such as tumors or fractures with remarkable precision (Kasula, 2023). Tools like IDoctor assist in disease diagnosis by analyzing medical images, while AI models predict disease risks based on patient data, enabling early preventive measures. In clinical documentation, AI auto-generates medical records and drafts post-visit notes, reducing administrative burdens. AI systems also analyze large datasets to provide diagnostic insights and identify high-risk patients, supporting timely interventions. ‘’AI predicts outcomes through learning from the acquired data of various sources’’ (Yang, 2022). For revenue cycle management, AI automates insurance claims processing and patient data management, speeding up workflows and minimizing delays. Predictive analytics optimizes supply chain management, ensuring adequate stock levels of critical supplies. These enhancements improve diagnostic accuracy, operational speed, and overall patient care.

Despite AI's benefits, significant risks must be considered. Algorithmic bias can lead to healthcare disparities, as seen in AI algorithms that underestimate risks for certain patient groups (Babushkina, 2023). Data quality issues, such as incomplete or inconsistent electronic health records, can result in incorrect predictions and recommendations. Technical glitches and system failures pose serious risks, potentially leading to incorrect diagnoses or treatment errors. Overreliance on AI may reduce healthcare providers' clinical judgment. High-profile incidents, such as AI systems with higher error rates than specialists, highlight these risks. Robust oversight and accountability frameworks are essential to ensure AI's safe and effective use in healthcare.

Though Shaheen (2021) provides valuable insights into the technical aspects of AI-assisted surgery, the study might be limited by its relatively small sample size, which could affect the generalizability of the findings. Zeb et al. (2024) offer a comprehensive overview of AI applications in healthcare, yet their work may be critiqued for primarily focusing on theoretical potentials rather than practical implementations, potentially overlooking real-world challenges. Yang (2022) highlights the benefits of AI in medical imaging but could be seen as somewhat optimistic, with a tendency to emphasize positive outcomes while possibly underreporting the limitations and adverse effects. Despite these potential shortcomings, all three sources contribute meaningfully to the discourse on AI in healthcare. They provide detailed analyses and recommendations that are valuable for researchers and practitioners. Their transparent methodologies and inclusion of both benefits and challenges demonstrate a commitment to advancing knowledge in the field. The works collectively underscore the transformative potential of AI while acknowledging the need for continued research and careful implementation.

AI is transforming healthcare delivery by enhancing patient engagement, enabling personalized treatment, and improving diagnostic accuracy and operational efficiency. However, challenges like algorithmic bias, data quality issues, and technical risks must be addressed. Collaboration among healthcare professionals, policymakers, and technology developers is crucial to establish ethical and safe AI practices. The future of healthcare lies in AI serving as a powerful ally to human practitioners. By ensuring responsible development and implementation, AI can create a more efficient and responsive healthcare system, ultimately improving health outcomes and enhancing well-being in the 21st century.