Demand of the question Introduction. Contextual Introduction. Body. Various potential application of artificial intelligence. Conclusion. Way forward. |
Artificial Intelligence (AI) is quickly evolving and is already being used to support and improve health services in many high-income countries. AI holds great promise for improving the delivery of health services in resource-poor settings. Further research and investments are needed to accelerate its deployment in such settings.
Some of the potential applications of AI in public health are:
- Diagnostics and screening: Artificial Intelligence can help in identifying or predicting diseases based on expressed symptoms. It will not only reduce diagnostic errors but will diagnose precisely based on patient’s history.
- Health worker performance and productivity: Artificial Intelligence will track the data captured by health workers, and use it to direct their efforts where they are most needed. This will enhance their efficiency and productivity. It will also reduce workload.
- Improving client adherence: Artificial Intelligence will help in identifying patterns and gaps in people’s health-seeking behaviour and will suggest who might drop out of a health programme or course of treatment. This will improve public health and will reduce unnecessary expenditure.
- Health informatics: Health informatics describes the acquisition, storage, retrieval and use of healthcare information to improve patient care across interactions with the health system. Health informatics can help shape public health programmes by ensuring that critical information is available for making sound policies and programme decisions.
- Electronic medical records (EMRs): EMRs, which are digital versions of patient and population health information, are an important source of data for health informatics. Their use has become much more prevalent in low-resource settings, which in an era of networked computers, has expanded potential applications of AI to improve public health informatics and decision making.
- Cloud computing: The expansion of cloud computing has led to the expansion of AI applications for health. Cloud computing refers to the use of a network of remote servers to store, manage, access and process data rather than a single personal computer or hard drive. While EMRs can be maintained in the cloud with adequate privacy and security precautions, cloud computing can be used with a multitude of data related to public health. Researchers recently tested a cloud computing application using patient data that aimed to improve interactive voice response telephone calls for managing non-communicable diseases.
- Mobile health: mHealth uses mobile and wireless technologies to achieve health objectives. The rapid availability and expansion of mobile phones in low-income countries has created several opportunities for using these technologies to support health efforts. Mobile phones have been used by community health workers (CHWs) to improve the provision of health services within resource-poor settings. Mobile phones have also been used to communicate health information to patients in resource-poor settings when face-to-face interactions are not feasible. The use of short message service to address demand-side barriers to vaccination and improve immunisation coverage has been thoroughly documented through randomised controlled trials in settings.
In India where public health system is not robust and millions live under poverty, Artificial Intelligence can ensure accessibility to public health. Rural areas where doctors are less can be reached using technology and artificial intelligence. Also artificial intelligence can reduce diagnostic errors but will also provide person centric services.