From present gains to future promise: How AI is reshaping healthcare systems

A sweeping editorial review published in Bioengineering under the title “Artificial Intelligence in Public Health: Bridging Today’s Trends with Tomorrow’s Possibilities” (Bioengineering 2025, 12, 559) provides a panoramic view of how artificial intelligence (AI) is reshaping healthcare across disciplines. 

The report synthesizes 26 recent studies exploring AI's contributions to diagnostics, patient management, epidemiology, telemedicine, and medical ethics. Compiling findings from multiple international research teams, the publication underscores AI's pivotal role in building a more predictive, equitable, and efficient public health landscape.

How is AI enhancing clinical decision-making and patient outcomes?

AI’s integration into patient diagnosis, treatment, and prognostics is one of the report’s most significant themes. Multiple studies analyzed within the editorial demonstrate how machine learning (ML) models, natural language processing (NLP), and neural networks are already driving breakthroughs in clinical accuracy and patient stratification.

For example, machine learning algorithms were used to predict delayed medical treatment behavior among oral cancer patients in China, enabling early intervention by identifying social and systemic risk factors. In emergency departments, ML models based on standard biochemistry tests proved capable of predicting short- and long-term patient mortality, aiding triage decisions.

Radiomics, the extraction of quantitative features from medical images, combined with AI, showed high accuracy in predicting progression-free survival in glioma and non-small cell lung cancer (NSCLC) patients. Meanwhile, deep learning models like dual DCNNs were used to classify brain tumors from MRI scans with superior accuracy compared to conventional methods.

AI was also applied in obstetrics to detect hemorrhage-related organ dysfunction using machine learning thresholds based on coagulation data. These findings collectively confirm AI’s potential to support faster and more personalized care while reducing the cognitive load on clinical staff.

What new frontiers are being explored in public health and preventive care?

Beyond diagnostics, the editorial highlights AI’s capacity to expand preventive care and health monitoring, particularly in underserved or crisis-prone environments. A standout study used YOLOv8, an advanced object detection algorithm, to track indoor movement and contact rates in hospitals and schools. By mapping real-time interpersonal interactions, the system facilitates more precise modeling of airborne disease transmission and informs spatially-targeted containment strategies.

In pediatric care, ML models predicted length of stay in intensive care units, helping optimize staff allocation and bed management. Ensemble ML frameworks were also employed to classify injury types using national surveillance data, with some models achieving near-perfect accuracy.

Telemedicine and mobile healthcare also benefited from AI innovations. Studies reviewed in the report discuss AI-enhanced mobile radiology for breast cancer screening in remote areas and AI-driven virtual companions for people with dementia, fostering engagement and reducing loneliness in long-term care settings.

AI chatbots emerged as low-cost, scalable interventions for women’s mental health and reproductive wellness. These systems proved effective in addressing anxiety, depression, and behavioral guidance, particularly for individuals lacking access to consistent clinical care.

What ethical and logistical challenges must be addressed to sustain AI integration?

While the reviewed studies champion AI’s transformative potential, the editorial also draws attention to critical implementation hurdles, particularly around ethical governance, data security, and clinical workflow compatibility.

Several studies underscore concerns over algorithmic bias, outdated model data, and the potential dehumanization of care in AI-dominated environments. For instance, despite AI’s diagnostic success in cardiology, maintaining patient empathy remains a challenge. Likewise, in teledermatology, the lack of standardized app validation and cybersecurity protocols raises safety and legal questions.

The review stresses the need for transparent regulatory frameworks and interdisciplinary standards to support AI deployment in sensitive areas like domestic violence detection and public health surveillance. Furthermore, machine learning models used in radiation dose prediction and causal variable analysis were noted for their reliance on small or incomplete datasets, indicating a broader need for scalable and diverse training data.

A recurring recommendation is the upskilling of healthcare professionals to operate AI tools effectively without disrupting established care pathways. Also emphasized is the importance of community-specific tailoring to ensure that AI tools serve populations equitably, particularly those in marginalized or rural regions.