Diagnosis is both one of the most critical and one of the most error-prone elements of medical care. Traditional diagnostic processes rely heavily on the experience and judgment of individual clinicians, which, while invaluable, are also subject to fatigue, cognitive bias, and variability in training. The magnitude of this issue is significant; a recent study in the United States estimated that nearly 800,000 Americans annually suffer death or permanent disability from diagnostic errors—specifically, 371,000 deaths and 424,000 permanent disabilities9.

Obtaining symptoms accurately and carefully, considering candidate diseases thoughtfully, and making differential diagnosis is essential for reaching a correct diagnosis 34. However, in high-pressure settings such as emergency departments10, physicians are often forced to make rapid judgments, relying on intuitive Type 1 thinking rather than the deliberate, analytical Type 2 thinking described by Kahneman in Thinking, Fast and Slow11. While Type 1 thinking enables timely decisions, it increases vulnerability to cognitive biases and heuristic shortcuts such as overreliance on initial impressions or stereotyped expectations which can result in missed or delayed diagnoses, particularly in atypical presentations (e.g., young patients with myocardial infarction)134512. AI-assisted decision support systems could help reduce these misdiagnosis risks by systematically flagging all candidate diseases in the list, including atypical cases in risk algorithms, and promoting adherence to diagnostic protocols regardless of patient demographics.

Another critical but often underrecognized contributor to medical care-related patient harm is medication errors, which include simple mistakes in prescription, inappropriate drug combination, mis-dosing, or wrong administration method (by medical professionals or by patients) and is estimated to be approximately 6.5 per 100 admissions in acute hospitals. These errors often go unnoticed due to the complexity of medication regimens, overlapping contraindications, and fragmented communication among multiple doctors/prescribers. Consequences include adverse drug reactions, therapeutic failure, and the masking or misattribution of symptoms, which may further delay accurate diagnosis. Artificial intelligence–driven clinical decision support systems, such as AI-enabled drug interaction system, integrate patient-specific clinical, biochemical, and pharmacogenomic data to systematically identify and prevent such errors. By embedding these tools into clinical workflows can improve medication appropriateness, minimize drug-related confounders, and improve patient safety13.

The rapid acceleration of medical knowledge compounds these challenges. Since the “knowledgedoubling time” was estimated to be approximately 50 years in 1950 and became about 73 days in 202014, far exceeding human capability to memorize, process and apply latest information. Consequently, there is a growing need for AI support to reduce clinicians’ cognitive burden. General-purpose large language models (LLMs) such as ChatGPT and Gemini can provide information for the possible disease candidate list from free-text input with notable accuracy in simulated cases, but they are mostly positioned as tools that “offer suggestions”1516. In contrast, specialized tools such as Glass Health’s “Glass AI” generate structured assessment & plan documents, incorporating evidence-based differential diagnostic steps and treatment options, positioning them for more direct integration into clinical workflows—subject to professional oversight17.

AI is significantly redefining diagnostic imaging by accelerating the interpretation of X-rays, MRIs, and CT scans, thereby shortening time-to-diagnosis—an essential advantage, particularly important in emergency department. It also enhances diagnostic accuracy by learning from vast datasets and identifying subtle patterns and anomalies that human eyes might overlook, thereby reducing misdiagnoses and ensuring timely, correct treatment18. For example, AI algorithms can function as a second reader for mammograms, analyzing images with high accuracy to identify subtle signs of breast cancer that might be missed by the human eye, thereby enhancing diagnostic precision192021.

Beyond image interpretation, AI also streamlines imaging workflows, lowers costs, and supports personalized diagnostic strategies through risk stratification and predictive analytics. Furthermore, AI's predictive capabilities leverage historical data for early disease detection and risk stratification, while its role in personalized medicine allows for tailored diagnostic approaches and treatment plans based on patient-specific data, moving away from a one-size-fits-all model.

In pathology, AI automates labor-intensive processes such as object recognition, detection, and segmentation, allowing specialists to focus on complex interpretive tasks. Algorithms can screen large volumes of histopathology images with high accuracy, flag urgent cases, and triage normal cases to reduce unnecessary reviews. Beyond detection, AI can assess tumor aggressiveness from 3D imaging and might even predict molecular and genomic profiles from tissue samples, enabling more personalized treatment strategies. Integration with electronic health records enhances consistency, standardization, and inter-reader reliability, while reducing the risk of fatigue-related errors. In addition, it is becoming unrealistic to expect one or two pathologists in a single hospital to provide comprehensive diagnostic coverage across all organ systems. It is anticipated that in the near future, diagnostic services with the integration of AI will become increasingly centralized, with subspecialized pathologists convened at dedicated centers. Through the transmission of digital images, such a system would enable more accurate and reliable diagnoses. A major advantage of digitalization with AI lies in its capacity to facilitate this centralization. Once histopathological specimens are scanned and converted into digital images, they can be transmitted to specialized centers equipped with highly advanced expertise for diagnostic evaluation. This development represents a crucial step toward reducing regional disparities in access to high-quality pathology services22.

Recent evidence also highlights the potential of AI in under-represented fields such as pediatrics and mental health. In pediatric imaging, deep learning systems have demonstrated high accuracy in classifying childhood pneumonia from chest radiographs, helping to compensate for the shortage of pediatric radiologists in many regions23. Beyond imaging, machine learning models have been applied to the early screening of autism spectrum disorder (ASD), using behavioral features such as atypical or inconsistent eye contact, difficulties in forming peer relationships, and challenges in appropriate play, as assessed through standardized behavioral questionnaires, to support earlier diagnosis and intervention24. In mental health, randomized controlled trials of AI-powered digital platforms, including conversational agents, have reported reductions in depression and anxiety symptoms, suggesting their promise as scalable adjuncts to conventional therapy25. At the same time, these approaches face important limitations, including the need for robust validation across diverse populations, safeguarding patient privacy, and ensuring that digital tools augment rather than replace the therapeutic relationship.

AI is also advancing early detection and management of chronic and progressive diseases. Neurodegenerative disorders such as Alzheimer’s disease are another key area where AI has shown some promise8. Deep learning models applied to neuroimaging can identify early brain changes long before conventional methods, offering opportunities for earlier intervention. Similarly, in chronic conditions like diabetes, AI can integrate lifestyle metrics, laboratory data, and treatment history to predict complications such as nephropathy, neuropathy, or retinopathy, thereby enabling preventive action8.

Wearable devices connected with AI extend early diagnostic capabilities beyond clinical settings. These devices can continuously monitor physiological parameters including heart rate, oxygen saturation, pulse regularity, and sleep patterns to detect conditions like atrial arrythmia or sleep apnea in real time138. Certain smartwatches have achieved high accuracy in atrial fibrillation detection using photoplethysmography combined with machine learning26, facilitating earlier intervention and potentially reducing stroke risk. However, the LOOP Study found that AF screening in older adults did not significantly reduce stroke or systemic embolism compared with usual care27. Although the data are still controversial, cerebral infarction attributable to atrial fibrillation represents a major determinant of long-term disability and subsequent dependence on nursing care28. Hence the clarification of them is extremely important in public health. Beyond its direct clinical consequences, cerebral infarction increases healthcare and long-term care expenditures and imposes considerable physical, psychological, and financial burdens on patients’ families28. Accordingly, the prevention of atrial fibrillation–related cerebral infarction constitutes a critical public health priority, particularly in the context of rapidly aging societies1.

Treatment paradigms are rapidly shifting toward a more personalized model with AI at the center of this transformation. In drug discovery, AI also transforms the traditionally slow and expensive process with identifying promising molecular targets, predicting drug–drug interactions, and even repurposing existing medications for new indications. This reduces development timelines and costs, improving accessibility to innovative therapies.

Precision medicine is another area where AI has demonstrated transformative potential. By combining omics information and microbiome data as well as lifestyle and environmental data, AI can help determine the most-effective treatment plan for individual patients rather than relying solely on generalized guidelines348. For instance, AI can assist oncologists in selecting targeted therapies that are most likely to be effective based on a tumor’s specific genetic mutations1.

Beyond pharmacological approaches, AI enables more effective management of chronic diseases. Wearable sensors connected to AI provide continuous feedback on patient health, offering personalized lifestyle recommendations and adjusting treatment plans in real time8. This proactive, data-driven approach empowers patients to take an active role in their care, while enabling clinicians to intervene before complications occur or progress.

AI is also reshaping mental healthcare. By analyzing patterns in speech, writing, or social media activity, AI systems can detect early signs of depression, anxiety, or other mental health problems although it is notable that AI-based CBT chatbots are recommended only for mild depression, not for moderate to severe cases. Predictive models can identify individuals at risk of suicide, enabling targeted outreach248. AI-powered chatbots and virtual assistants offer 24/7 access to evidence-based toolssuch as cognitive behavioral therapy, offering scalable, stigma-free, and accessible mental health support29. However, WHO 2024 guideline is cautious to use these chatbots for a wide range of depression30.

One of AI’s most immediate benefits in healthcare is its ability to reduce the workload of medical professionals, including physicians, nurses, and pharmacists. Among these applications, AI-powered speech-to-text systems can transcribe consultations in real time, automatically generating structured clinical notes and nursing records148. This is particularly valuable given that nurses spend an estimated 20–30% of their working hours on documentation, such as recording vital signs and procedures, which could otherwise be spent directly with patients3132. In addition, natural language processing (NLP) tools can further summarize patient histories, extract key information from electronic medical records, and draft discharge summaries, correspondence, or instructions for patients and healthcare providers in other facilities.

Operational efficiencies extend beyond documentation. AI-driven scheduling systems can optimize patient flow and staff allocation, while in pharmacy management, algorithms can track inventory, forecast demand, and detect prescribing errors before they reach the patient. For patients, these systems shorten waiting times and reduce administrative barriers, enabling more personalized interactions1829. Beyond the hospital settings, AI-powered remote monitoring allows patients with chronic illnesses to remain at home under close supervision, minimizing the need for frequent visits348. Mobile health applications integrated with AI platforms can deliver real-time test results, medication reminders, and secure access to medical records, enhancing transparency and patient engagement13412.

Most importantly, by alleviating repetitive administrative burdens, AI allows clinicians to focus on the human aspects of care—listening, explaining, and building trust—all of which are central to patient satisfaction and improved outcomes.

The implementation of AI in the healthcare is accelerating globally, though approaches vary by each country and healthcare system278. In Japan, for example, the “Innovative AI Hospital System” was launched to promote hospital digitalization, improve diagnostic accuracy, and reduce workload of medical workers1. Initiatives include constructing large-scale health/medical databases, developing predictive algorithms for disease risk, and implementing AI-assisted guiding and diagnostic tools of endoscopy and colonoscopy as well as liquid biopsy for cancer screening.

Other countries are pursuing similar strategies, incorporating AI into national health infrastructures. Some prioritize telemedicine platforms with AI-based triage tools to extend access in remote areas. Others emphasize patient-facing innovations, such as AI avatars for standardized informed consent discussions, or mobile applications that notify patients of appointment delays and streamline payments (Nishimura Y. et al., unpublished data). These early applications highlight the versatility of AI, demonstrating its impact not only on clinical diagnosis and treatment but also on organization, communication, and operational workflows that shape the patient journey.

Emerging evidence from low- and middle-income countries (LMICs) highlights AI’s huge potential to improve both equity in and access to maternal and neonatal care. A systematic review shows AI implementation in LMICs have enhanced clinical capabilities such as diagnostic accuracy and covering specialist shortages33, and AI tools have improved imaging interpretation and decision-making infrastructure in these regions34. Specifically, it was reported that AI-enabled point-of-care ultrasound analysis could improve gestational age estimation, provider confidence, and care-seeking behavior in LMIC antenatal settings, reflecting anticipated benefits35. Additionally, AI-augmented fetal monitoring in Malawi has been shown in observational work to significantly reduce intrapartum stillbirths and neonatal mortality36. These examples demonstrate how context-appropriate AI interventions can improve outcomes in resource-constrained maternal and neonatal care settings.

The next phase of AI in healthcare/medical care is likely to emphasize integration, combining multimodal data streams, including multi-omics, imaging, wearable sensors and social determinants of health, into unified platforms that can support more holistic decision-making. Within this context, emerging technologies such as digital twins, which are virtual models of individual patients, could allow clinicians to simulate treatment scenarios and predict outcomes with unprecedented precision78. Large language models may streamline communication between healthcare providers and patients by generating tailored explanations and educational materials instantly, while AI-enabled robotics could enhance surgical precision and provide advanced rehabilitation support1234.

Perhaps and most importantly, AI has the potential to reduce global health disparities45. Cloud-based platforms and increasingly affordable devices could extend expert-level diagnostics and monitoring to remote and underserved populations. Paired with equitable access policies, these advances could help standardize care quality worldwide. The ultimate vision is a healthcare system that is efficient, cost-effective, and profoundly human—one in which technology augments clinicians’ capacity to exercise uniquely human judgment, creativity, and compassion.

To concisely summarize the most important insights presented in this review, Table 1 highlights domains where AI and digital tools can significantly enhance efficiency, alongside areas of clinical practice that rely on uniquely human judgment, empathy, and interpersonal engagement.