Reseña del libro "MEDICAL AI FOR STUDENTS AND CLINICIANS 2026-2027 (en Inglés)"
MEDICAL AI FOR STUDENTS AND CLINICIANS 2026–2027 is a comprehensive, clinically focused textbook designed to help healthcare learners and professionals understand the rapidly evolving role of artificial intelligence in modern medicine. Rather than treating AI as a replacement for clinical expertise, this book shows how it can support better reasoning, improve information processing, strengthen decision-making, and enhance healthcare delivery when used with appropriate evidence, validation, governance, and professional oversight. Written for medical students, clinicians, nurses, allied health professionals, postgraduate trainees, educators, public health learners, and other healthcare professionals, this book takes readers from essential foundations to real-world clinical applications. Inside, readers will explore: Core AI and machine-learning concepts without unnecessary technical complexity Medical data, algorithms, neural networks, deep learning, and modern AI architectures Clinical decision support, predictive analytics, and risk-stratification systems Artificial intelligence in radiology, pathology, laboratory medicine, and electronic health records Wearables, remote patient monitoring, digital biomarkers, and physiological data AI applications across emergency medicine, internal medicine, surgery, critical care, pediatrics, obstetrics, geriatrics, neurology, mental health, and rehabilitation Generative AI and large language models in healthcare, including their capabilities, limitations, hallucinations, and verification requirements Sensitivity, specificity, predictive values, discrimination, calibration, validation, and model performance Bias, fairness, uncertainty, dataset shift, model drift, and population generalizability Clinical implementation, workflow integration, automation bias, and human factors Regulation, governance, professional accountability, privacy, cybersecurity, and health-data stewardship Health equity, global access, patient communication, and shared decision-making Emerging technologies, including multimodal AI, intelligent agents, advanced clinical automation, robotics, and digital-twin concepts The knowledge and competencies required to become an AI-literate healthcare professional Throughout the book, technical concepts are continually connected to clinical reasoning and patient safety. Readers are encouraged to ask practical questions: What data entered the system? How was the model validated? Does it apply to this patient population? What uncertainty remains? Could bias influence the result? What happens when the clinician and the algorithm disagree? Who remains responsible for the final decision? Clinical tables, figures, practical examples, section exercises, multiple-choice questions, short-answer questions, clinical scenarios, explanatory rationales, and professional reference frameworks reinforce understanding and encourage active learning. The central message is clear: AI can recognize patterns, generate information, estimate risk, and support clinical work—but it does not remove the need for medical knowledge, critical thinking, ethical judgment, patient-centered communication, or professional accountability. MEDICAL AI FOR STUDENTS AND CLINICIANS 2026–2027 equips readers not simply to use artificial intelligence, but to understand it, evaluate it, question it, and apply it responsibly in an increasingly AI-augmented healthcare system.
