Abstract: AI models are being applied to several components of healthcare including diagnostics, drug development, personalized healthcare plans and so on. The large datasets which rapidly rise in complexity and volume form part of big data, provide the potential for ML models to make significant progress in diagnostics, medical image interpretation and treatment recommendations. The introduction of AI across the various departments of the healthcare industry has already raised efficiency and dropped prices. Covid 19’s exponential growth rate completely overwhelmed the healthcare sector's capabilities. AI and robotics became paramount in boosting efforts to detect, identify the risk of and monitor the virus, and develop and deliver the vaccine throughout the planet. The systems that did well generally had large datasets, one modality and did well in testing with foreign data. Currently, low efficiency and high costs make research difficult. With big data being produced at astronomical rates, we should theoretically be making constant discoveries and advancements. However, only part of this data is currently being processed and studied. Attempts are being made to resolve this gap by leveraging AI tools
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