review · European journal of medical research
Artificial intelligence offers promising solutions to major pressures facing global healthcare, including rising costs, workforce deficits, and systemic disparities in access, particularly in low- and middle-income regions. By processing complex datasets such as electronic health records, genomic profiles, and medical imaging, machine learning algorithms can enhance early disease detection, personalise therapies, and aid drug discovery. In remote and resource-limited environments, lightweight models, wearable biosensors, and mobile diagnostic systems present practical means to bridge service gaps. However, achieving dependable clinical integration demands careful attention to algorithmic bias, data privacy, model interpretability, and clear regulatory standards. Ensuring clinicians are trained in AI literacy and maintaining human supervision are critical steps. When implemented ethically as a collaborative tool rather than a replacement for staff, AI can expand access to care, reduce clinical errors, and improve treatment outcomes worldwide.
Health systems worldwide grapple with staffing shortages, escalating costs, and uneven access to quality care. Deploying artificial intelligence thoughtfully can relieve pressure on medical professionals, minimise diagnostic errors, and extend modern diagnostics into underserved communities. Addressing critical challenges such as bias and privacy ensures that technological progress translates into safe, equitable, and sustainable patient care across diverse economic settings.
Commercial opportunities encompass mobile diagnostic software, wearable biosensors, and lightweight machine learning tools suited for low-resource clinics and remote patient care. Primary users include healthcare providers, clinicians, and health administrators seeking to streamline diagnostics and treatment planning. Because the scope covers broad applications alongside ongoing barriers like regulatory oversight, data privacy, and bias, practical readiness spans from early development through to deployable telemedicine and wearable solutions requiring clinical validation.
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Healthcare systems worldwide face growing challenges, including rising costs, workforce shortages, and disparities in access and quality, particularly in low- and middle-income countries. Artificial intelligence (AI) has emerged as a transformative tool capable of addressing these issues by enhancing diagnostics, treatment planning, patient monitoring, and healthcare efficiency. AI's role in modern medicine spans disease detection, personalized care, drug discovery, predictive analytics, telemedicine, and wearable health technologies. Leveraging machine learning and deep learning, AI can analyze complex data sets, including electronic health records, medical imaging, and genomic profiles, to identify patterns, predict disease progression, and recommend optimized treatment strategies. AI also has the potential to promote equity by enabling cost-effective, resource-efficient solutions in low-resource and remote settings, such as mobile diagnostics, wearable biosensors, and lightweight algorithms. Successful deployment requires addressing critical challenges, including data privacy, algorithmic bias, model interpretability, regulatory oversight, and maintaining human clinical oversight. Emphasizing scalable, ethical, and evidence-driven implementation, key strategies include clinician training in AI literacy, adoption of resource efficient tools, global collaboration, and robust regulatory frameworks to ensure transparency, safety, and accountability. By complementing rather than replacing healthcare professionals, AI can reduce errors, optimize resources, improve patient outcomes, and expand access to quality care. This review emphasizes the responsible integration of AI as a powerful catalyst for innovation, sustainability, and equity in healthcare delivery worldwide.
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DOI: 10.1186/s40001-025-03196-w
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