The integration of Artificial Intelligence (AI) into healthcare promises
improved medical evaluation, provided therapeutic solutions, and enhanced patient
care. Various AI branches, including machine learning, deep learning, and computer
vision, adeptly handle vast healthcare datasets ranging from electronic health records to
wearable data. These techniques extract vital patterns, forecast early-stage diseases, and
personalize patient treatments. In diagnostics, AI tools excel at identifying diseases and
predicting patient outcomes by automating image readings and pinpointing health risks.
AI also optimizes healthcare logistics, resource allocation, and overall patient care,
reducing clerical tasks and promoting data-driven decisions. Yet, there are challenges.
Concerns about data privacy, legislative compliance, ethics, and the need for
transparent AI results are paramount. Addressing these is crucial for successful AI
integration in healthcare. In essence, AI's integration into healthcare promises
revolutionary diagnostic and therapeutic advances. Navigating the challenges requires
collaboration between medical experts, AI specialists, lawmakers, and ethicists to fully
realize AI's transformative potential.
Keywords: Artificial intelligence, Deep learning, Healthcare, Machine learning, Patient data.
