Aims and Scope

ARC: Journal of AI in Clinical Practice publishes studies of the intersection of artificial intelligence and clinical service delivery; and its goal is provide a practical view of AI functionality in real healthcare environments - not strictly theoretical or laboratory-type experiments.

What catches our attention most? Work showing how AI actually functions in real medical settings - clinics, hospitals, patient neighborhoods. Think studies sharing what went well, where things fell short, surprises that popped up, moments of clarity after struggle. Progress isn’t always clean. If the findings feel truthful, even if incomplete, they still matter here.

Research that advances knowledge of how AI can enhance everyday care, assist in decision-making, and enhance healthcare delivery in practical contexts is highly valued by ARC.

ARC's purposely wide reach reflects the various ways artificial intelligence is impacting contemporary healthcare. Submissions from a variety of clinical, technological, and multidisciplinary backgrounds are encouraged as long as the work clearly relates to actual clinical practice.

Research on the use of artificial intelligence (AI) systems in radiology, pathology, cardiology, cancer, dermatology, and other medical specialties for clinical decision-making, illness risk prediction, treatment planning, triage, and diagnostic support.

Research on machine learning and deep learning models, including dataset creation, validation using clinical data, performance assessment, bias analysis, and generalizability.

Improvements in clinical processes, patient flow management, resource planning, regular task automation, and clinical documentation assistance were the main areas of attention.

Research on early warning systems, risk alarms, safety monitoring, clinical decision support systems, and efficient clinician-AI system collaboration.

Conversations about fairness, explainability, transparency, ethical and responsible AI use, regulatory issues, and obstacles to clinical application.

Research on wearable technology, natural language processing, electronic health record analytics, remote patient monitoring, and integration of multimodal clinical data.

Research on training frameworks, curriculum design, simulation-based learning, workforce readiness for AI-enabled care, and AI literacy for physicians.

Quality improvement initiatives, implementation reports, pilot studies, and post-deployment assessments of AI systems in clinical settings.

Clarity and direction on clinical AI research and practice are provided via systematic reviews, scoping reviews, expert commentary, and methodological discussions.

ARC welcomes articles that provide useful insights, clarify difficulties, and test hypotheses. It doesn't matter if the research originates from a research lab, a small clinical team, or a huge healthcare system as long as it makes a significant contribution to our understanding of how AI can be applied ethically and successfully in actual clinical care.