Today, Artificial Intelligence (AI) is revolutionizing the industry, helping doctors and healthcare providers deliver better, faster, and more personalized care. This is what we call “smart healthcare”. A review published in MDPI discusses artificial intelligence applications in smart healthcare.

Traditionally, healthcare has focused on doctors and specialists. But smart healthcare flips that model by putting the patient at the center. Thanks to AI, smart healthcare uses technology to enhance the entire experience, making it more personalized, accurate, and efficient. AI analyzes vast amounts of health data, helping doctors make faster decisions, from diagnosing diseases to creating tailored treatment plans.

AI is transforming healthcare in major ways. It helps with disease prediction and prevention by using patient data to predict and prevent outbreaks and health issues like cancer and COVID-19. Diagnostic imaging benefits from AI, as it analyzes X-rays, CT scans, and MRIs to spot abnormalities that might be missed by the human eye, speeding up diagnoses. AI also helps create personalized treatment plans, especially in cancer care, by tailoring treatment to your unique genetic and health data. Virtual health assistants powered by AI track symptoms, provide health advice, and help manage chronic conditions—all from your phone. Remote patient monitoring through wearables alerts doctors to any health issues in real-time, ensuring quicker care. AI also speeds up drug discovery by predicting the effectiveness of drugs and bringing life-saving medications to market faster. In the operating room, AI-assisted robotic surgery enhances precision and reduces recovery times. NLP for EHRs (Electronic Health Records) helps doctors quickly extract critical information from patient records, improving decision-making. On the mental health front, AI supports behavioral health by detecting issues early through data analysis from wearables and social media, allowing patients to get the help they need faster. AI also assists with clinical trial matching, connecting patients to the right studies and making research more efficient. These AI applications are transforming healthcare, making it smarter, faster, and more personalized than ever before.

But there are challenges! Data integration is difficult due to incompatible systems, and managing massive amounts of data requires tools like Hadoop. Real-time decision-making is slowed by cloud delays, which makes edge computing a better solution. AI’s lack of transparency raises trust issues, while security risks threaten patient privacy. Bias, ethical concerns, and EHR integration need to be addressed, and scaling AI for large systems remains tricky. In rural areas, weak internet connectivity limits AI’s potential, requiring offline models. Overcoming these challenges will unlock AI’s full potential in healthcare.

AI is the future of healthcare, but addressing these challenges is key to unlocking faster diagnoses, better treatments, and smarter care.