Artificial Intelligence (AI) based techniques are revolutionizing healthcare. Several wide-ranging applications involve processing massive collections of electronic health records, data from tele-monitoring devices, and medical imaging data. Healthcare AI has great potential; it can transform science and practice. AI models can be invaluable in guiding the improvements and innovations in biomedical sciences. Large Language Models (LLMs) are changing the paradigm of how science can be done. Further, AI is being used to bridge the gap between basic biology and health. Innovations in healthcare are sorely needed; healthcare offers several opportunities for discoveries that AI can catalyze. In practice, the heavy lifting of building these AI models is done by machine learning and deep learning techniques. These advances are owed to the improvements in computing infrastructure, the proliferation of data, and immense innovation in machine learning and deep learning theory, models, algorithms, and toolkits. As a consequence, it has become relatively easy to implement healthcare models. The democratization of tools is lowering the bar to deploying AI in healthcare, but at a critical cost. Charity at scale is difficult. Though it is now possible to create AI systems with great ease from the perspective of designing and implementation, this is not to say that putting such an AI product or service in the hands of the intended user and for the intended use is simple.