Transforming Healthcare with Technology in the AI Era (Part 2) | ASUS Pressroom

Healthcare worldwide is at a crossroads. Aging populations, chronic disease, and a global shortage of medical professionals are straining traditional systems. Artificial Intelligence offers a transformative path forward—reshaping how care is delivered, decisions are made, and resources are optimized. In this three-part series, I explore how AI is redefining healthcare: from continuous patient monitoring, to driving systemic efficiency, to building the foundations of a smart, AI-centric medical future.

In this second article, we focus on AI’s role as a systemic enabler. From medical imaging to predictive analytics, AI is addressing global healthcare challenges—optimizing workflows, reducing diagnostic errors, and helping institutions allocate resources more effectively. Here, we explore how these capabilities are reshaping the industry at scale.

AI as a Key Driver in Transforming the Healthcare Industry

A report by the World Economic Forum (WEF) found that the global healthcare system faces the dual challenges of an aging population and a shortage of medical personnel. Likewise, the World Health Organization (WHO) estimates a global shortage of nearly 18 million healthcare workers by 2030. At the same time, the number of chronic disease patients will continue to rise, expanding healthcare demands that existing resources struggle to meet. Traditional healthcare, reliant on manual judgment and operations, can no longer cope with the vast amount of medical record data and complex decision-making processes.

Smart healthcare is the solution to these problems. With its ability to effectively optimize processes and integrate data, it can not only enhance medical efficiency but also become a solution for the healthcare system by reducing human workload, improving diagnostic accuracy, and accelerating decision-making. Through the introduction of AI and technology, healthcare institutions can more effectively allocate limited resources, and physicians can dedicate their efforts to challenges requiring high-level professional judgment and critical clinical tasks. At the same time, they can also focus more on the quality of doctor-patient interactions, thereby improving healthcare quality and efficiency.

AI’s Role in Smart Healthcare

Currently, high-performance AI computing applications in smart healthcare can be broadly categorized into four types: medical image recognition, continuous physiological signal analysis, big data simulation and analysis, and genomics and drug development. These applications share a common characteristic: they rely on high-dimensional structured data with clear formats and field definitions, such as laboratory data, vital sign records, medical record fields, and medical image annotations. This high-quality clinical data serves as the optimal dataset for AI to perform comparisons, computations, and pattern learning.

One of the advantages AI brings to the field is medical image interpretation. The World Health Organization (WHO) emphasizes in its “Strengthening Diagnostics Capacity” policy that accurate diagnosis is central to clinical decision-making, with approximately 70% of treatment plans depending on diagnostic results. AI-assisted image interpretation can help specialists, such as radiologists, neurologists, and cardiologists, detect lesions more quickly and accurately, reducing misdiagnosis rates and increasing the likelihood of early disease detection.

Beyond imaging, another major strength of AI is the integration of, and insight into, multi-dimensional data. With the convergence of Electronic Health Records (EHR), multi-dimensional images, medical records, and genetic data, AI can process both structured and unstructured data simultaneously and quickly extract core information needed for clinical decision-making from vast amounts of data. For instance, the Johns Hopkins Malone Center utilizes AI systems for the early detection of sepsis, issuing alerts before the patient’s condition deteriorates by combining laboratory data, genetic information, and physiological parameters.

AI excels at organizing and analyzing multi-source, high-dimensional structured data, which helps to fill the gaps in today’s healthcare system. At the individual level, AI’s direct value lies in supporting personalized treatment plans. By integrating medical records, genetic data, lifestyle information, and physiological data, AI can simulate the effects of different treatment options on individuals, predict complication risks, and make more precise and tailored patient treatment plans.

For healthcare institutions, AI can enhance overall operational efficiency. For example, it frees up doctors and nurses to focus on clinical care by handling a significant portion of their tedious administrative and logistical tasks. This includes optimizing processes such as medical record documentation, patient flow prediction, surgical scheduling, and emergency department triage. For example, by analyzing patient health data with AI, the UK’s NHS Health Navigator system identifies high-risk patients and offers Proactive Health Coaching programs, enabling early intervention to reduce unexpected hospitalizations.

Finally, AI is accelerating the development of telehealth services. By combining mobile medical devices, automated monitoring devices (such as wearables), and 5G and IoT infrastructure—even in remote areas or regions with limited medical resources—healthcare accessibility and coverage can be greatly improved. The Mayo Clinic, for example, successfully reduced readmission rates by approximately 40% through its remote care services, ensuring that patient conditions can be monitored even when physicians are not physically present.

In the final article of this series, I will discuss how we can build the future of medical care through an AI-centric ecosystem that integrates technology, data, and clinical practice.