Dear Editor,
In an era characterized by increasing disease complexity and rising patient expectations, effective interdisciplinary collaboration has become indispensable to the delivery of high-quality healthcare (1). Nevertheless, traditional approaches to care coordination often encounter challenges related to fragmented communication, conflicting clinical recommendations, and poor continuity of care. In this context, the integration of advanced technologies such as Artificial Intelligence (AI) is no longer merely an innovation but an influential component of contemporary healthcare systems, offering new opportunities to support care coordination and facilitate interdisciplinary collaboration (2).

One of the most significant contributions of AI is its capacity to enhance care across the continuum of healthcare delivery. By synthesizing diverse sources of information, including electronic health records, laboratory findings, clinical notes, discharge summaries, and care plans, AI can generate a comprehensive understanding of patients’ health status and support more informed clinical decision-making (3-5). In personalized medicine, AI enables the integration and analysis of genetic, biochemical, and behavioral data to facilitate individualized treatment strategies that extend beyond conventional symptom-based approaches (6, 7). Such precision may improve therapeutic outcomes while reducing the risk of adverse drug reactions (3, 8). Likewise, AI has transformed disease diagnosis through the rapid and accurate interpretation of complex clinical data, particularly medical imaging such as computed tomography scans, magnetic resonance imaging, and radiographs. Machine learning, deep learning, and fuzzy logic approaches have demonstrated substantial potential in detecting cancers, cardiovascular diseases, and neurological disorders, including Alzheimer’s and Parkinson’s diseases, often with performance comparable to that of expert clinicians (9). Furthermore, AI-enabled wearable technologies and Internet of Things (IoT) devices facilitate continuous patient monitoring through real-time transmission of health information. These innovations have shown promise in a range of applications, including glycemic control, fall-risk prediction, and the early identification of psychological crises and suicidal ideation (3, 5, 10).
Beyond its technical capabilities, AI may also offer opportunities to support interdisciplinary collaboration. By integrating information from multiple sources into a shared evidence base, AI may facilitate communication and collaborative decision-making among healthcare professionals while promoting a common understanding of patients’ conditions across physicians, nurses, pharmacists, and other members of the healthcare team. Such comprehensive insights could support more coordinated care planning and enable more timely and synchronized responses to changing patient needs, potentially reducing the risk of adverse events and medication-related errors (2, 11, 12). Consequently, AI appears to hold promise not only for enhancing diagnostic and therapeutic accuracy but also for fostering more integrated and patient-centered healthcare delivery.
Nevertheless, the growing implementation of AI in healthcare is accompanied by significant ethical, legal, and professional challenges. Concerns related to data privacy, algorithmic bias, transparency, accountability, and informed consent require ongoing scrutiny and robust regulatory oversight. In addition, unequal access to AI technologies across low- and middle-income countries raises important concerns regarding global health equity (13). Another critical consideration is the possibility that excessive reliance on algorithm-generated recommendations could inadvertently reduce direct communication and shared clinical reasoning among healthcare professionals. In light of this concern, AI should not be viewed as an autonomous decision-maker. Rather, its greatest value is likely to lie in its ability to complement human intelligence, support clinical judgment, and facilitate informed decision-making within interdisciplinary healthcare teams (1). When implemented responsibly, AI can enhance both the quality of clinical care and the collaborative processes that underpin effective healthcare delivery.