Health and Artificial Intelligence

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Artificial intelligence (AI) is a comprehensive set of technological systems designed to simulate cognitive functions such as learning, reasoning, and decision making. In healthcare, computer use was previously limited to basic instrumental tasks such as record keeping and billing; today, thanks to machine learning and deep learning technologies, systems have acquired the ability to learn from data and solve complex medical problems without explicit programming. Alongside the Industry 4.0 and Healthcare 4.0 processes, healthcare services are undergoing structural transformation through the integration of virtualization, personalization, and autonomous systems.^【1】 This evolution aims to reduce the workload of healthcare professionals, minimize medical errors, and enhance care quality.

Clinical Diagnosis, Assessment, and Treatment Applications

In medicine, artificial intelligence supports expert performance in imaging-focused specialties such as radiology, pathology, and dermatology. Deep learning algorithms detect abnormalities in medical images with high precision, improving accuracy rates in areas such as breast cancer screening. In clinical processes, AI contributes to the development of personalized treatment plans by analyzing genetic and environmental data and accelerates drug development.

• Pandemic Management: During the COVID-19 pandemic, algorithms were used to predict the rate of disease transmission and identify target molecules for vaccine development.
• Robotic Surgery: Systems such as Da Vinci enhance surgical safety by providing surgeons with millimeter-level precision and haptic feedback, while also reducing recovery times.

Interdisciplinary Technological Integration

Artificial intelligence offers specialized solutions across various healthcare disciplines beyond medicine:

• Physical Therapy: Patient exercise adherence is monitored through gait analysis, early detection of fall risk, and tele-rehabilitation applications.
• Nursing: Efficiency is improved through continuous monitoring of vital signs, medication dosage management, infection control, and staff shift planning.
• Midwifery: Big data analytics provides decision support mechanisms for predicting high-risk conditions during pregnancy and personalizing postnatal care.

These technologies expand the time healthcare professionals dedicate to direct patient care by automating routine and administrative tasks.

Health Management and Economics

In hospital and national health system management, AI holds strategic importance for efficient resource allocation and cost management. Decision support systems enable data-driven management in processes such as capacity forecasting, bed occupancy optimization, and drug utilization analysis. Natural Language Processing (NLP) technologies reduce documentation time and costs by automatically converting voice notes into text.^【2】 Algorithms integrated with e-Nabız, MHRS, and Decision Support Systems (DSS) contribute to the overall health economy by preventing unnecessary diagnostic repetitions.

Ethical, Legal, and Security Risks

The integration of this technology brings forth various ethical and legal debates. Systems may present false information, referred to as “hallucinations,” potentially delaying professional intervention. Moreover, demographic biases in training data—such as race, gender, and socioeconomic status—can lead algorithms to produce discriminatory outcomes.

Legally; the allocation of responsibility in cases of misdiagnosis or improper application has not yet been clearly defined within a legal framework. This situation has prompted discussions in legal doctrine around concepts such as “strict liability” and “electronic personhood.” Data privacy, patient confidentiality, and cybersecurity risks remain primary barriers to the secure use of these systems.

Future Perspective

The future of artificial intelligence in healthcare lies in its role not as a replacement for physicians, but as an assistant that enhances clinical decision-making. While AI holds advantages in data processing speed, the human factor remains essential in areas requiring clinical intuition, physical examination, and emotional depth. In future healthcare systems, the ability to critically evaluate and validate algorithmic recommendations will be a critical competency. Safe integration is achievable through transparent algorithms, oversight processes, ethical standards, and multidisciplinary collaboration.