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Medical practice has become transformed due to computerized patient healthcare charts. Electronic health record (EHR) systems are increasingly adopted in many developed countries; they also establish mobile health to allow mobile services in support with the usage of medicines for daily health routine checkups. EHR systems diminish errors of medication and improve coordination of care. The data extracted from EHR systems for statistics and research give support to many countries to achieve the goal for a high‐level achievement. Healthcare still captures the data in devices and is separately analyzed. Interoperability is a basic challenge that must be known about the full future of EHRs. One of the articles described the situation of young man, Roger, who came to the hospital in emergency grumbling with nausea and belly pain. This article showed 55 doctors to identify the usability of EHRs. These digital versions of paper charts are used by caretakers to record patients’ history, lab tests, early diagnosed prescriptions, disease background, and so forth, for medication doctor transfer in the EHR system and the search contained 500‐mg dosage of Tylenol because Roger was 26 years old. The doctor selected the appropriate option and prescribed the medication dose [16]. Availability of data is a basic element of EHR implementation, and EHR makes a perfect match for machine learning for fueling the data science operations. Machine learning is a remarkable milestone for fundamentals of EHR for example, data mining, NLP for document searching, transcription of report generation, data visualization, privatization and data, and predictive analysis. Thus we can say without AI we cannot imagine the medical world today.