Keterangan
The "quadruple aim" of healthcare—improving population health, improving patient and caregiver experiences, and lowering the rising cost of care—presents formidable obstacles for healthcare systems worldwide. Global healthcare expenditures, the burden of chronic diseases, and aging populations are making it harder for governments, payers, regulators, and providers to innovate and change healthcare delivery models. Furthermore, healthcare systems are forced to "perform" (provide efficient, high-quality care) and "transform" care at scale by incorporating real-world data-driven insights into patient care in the context of the current global pandemic.
Ringkasan
- Clinical AI applications aim to prevent disease, detect medical changes, accelerate diagnosis, and personalize treatment. Data is critical, as AI learns from existing information, differentiating it from human intelligence's capacity for original creation. Medical data sources include EHRs, medical imaging, pathology, genomics, patient-centered, and behavioral health data.
- Clinical AI outcomes encompass improved clinical decisions, disease prediction, and optimized treatment plans. Broader societal impacts include economic benefits, public health improvements, and improved healthcare access in developing countries, contributing to the concept of universal healthcare.
- Deep learning is predominantly utilized in healthcare due to the complexity and variability of medical data. AI's role in healthcare is not new, with early advocates like Dr. Eric Topol emphasizing its potential. Core AI branches include computer vision, robotics, natural language processing, and speech recognition, each having specific applications within healthcare.
- AI models are developed from algorithms, requiring data, training, and annotation, often executed by clinicians. AI will likely automate model training, potentially replacing some engineering and scientific roles. AI algorithms include neural networks, which emulate the human brain's structure and function.
- AI in healthcare can be categorized into preventive, diagnostic, and curative care. Preventive care involves symptom checkers, while diagnostic care includes smart ventilators and AI-based genome sequencing. Curative care covers surgical robotics and sensors for patient monitoring, especially in physiotherapy.
- AI adoption in healthcare hinges on saving time, enhancing screening capabilities, and ensuring remote accessibility. Building AI models requires problem identification, technical feasibility assessments, data collection, expert annotation, algorithm creation, and model training.
- Major challenges in AI adoption include trust issues, large amounts of unorganized data, biases, and a lack of standardization. A systematic approach to AI adoption involves selecting a pressing problem, designing a technology, and identifying a specific target population. Success stories include AI applications in oral cancer screening and collaborations with healthcare providers and academic institutions.
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