Digital Health and AI in Antibiotics Management and Antimicrobial Resistance Surveillance

Dear Colleagues,

This Special Issue explores how digital health technologies and artificial intelligence are transforming antibiotics management, antimicrobial stewardship, and resistance surveillance. It invites original research articles and reviews addressing clinical applications, research innovations, and regulatory challenges in this critical field.

Digital Tools in Antibiotic Therapy

Digital health solutions, including clinical decision support systems (CDSS) and mobile apps, optimize antibiotic prescribing by integrating guidelines and real-time data. Studies show artificial intelligence-enhanced CDSS reduces unnecessary prescriptions, shortens de-escalation times by up to 24 h, and improves adherence to protocols in settings like outpatient telemedicine. For instance, machine learning models predict resistance patterns with AUROC scores around 0.80, aiding empirical therapy in urinary tract infections and pneumonia.

AMS and Surveillance artificial intelligence and machine learning enable predictive analytics for resistance detection via biosensors and IoT networks, supporting One Health surveillance across clinical, veterinary, and environmental domains. Reviews highlight ML's role in antimicrobial stewardship programs, identifying low-risk cases for discontinuation (60% detection rate) and reducing broad-spectrum use by 17–28%. Original research can demonstrate hybrid artificial intelligence-biosensor systems for rapid AMR tracking, addressing data scarcity through scalable models.

Regulatory and Ethical

Considerations Emerging frameworks from EMA and FDA outline principles for trustworthy artificial intelligence in medicine, emphasizing risk-based oversight, transparency, lifecycle management, and data governance for tools in stewardship. These include human-centric design, explainability, and ethical data sharing to mitigate biases in AMR applications. Contributions should examine compliance with global initiatives like WHO's AMR Action Plan and challenges in equitable deployment.

Submission Scope:

We welcome original articles on empirical evaluations (e.g., RCTs of AI-CDSS) and systematic reviews synthesizing evidence on digital antimicrobial stewardship impacts. Topics span telemedicine integration, biosurveillance algorithms, and policy analyses, with a focus on multicenter data from diverse settings. Submissions undergo peer review; aim for concise, impactful manuscripts advancing evidence-based stewardship in the artificial intelligence era.