The World Health Organization (WHO) has issued its first report on Artificial Intelligence in Health, and suggested guiding principles for the same.
Background Information:
Artificial Intelligence (AI) refers to the ability of algorithms encoded in technology
to learn from data so that they can perform automated tasks without every step in the process having to be programmed explicitly by a human.
An AI system is a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy.
The various types of AI technology include machine-learning applications such as pattern recognition, natural language processing, signal processing and expert systems. Machine learning, which is a subset of AI techniques, is based on use of statistical and mathematical modelling techniques to define and analyze data. Such learned patterns are then applied to perform or guide certain tasks and make predictions.
AI can augment the ability of health-care providers to improve patient care, provide
accurate diagnoses, optimize treatment plans, support pandemic preparedness and
response, inform the decisions of health policy-makers or allocate resources within
health systems.
AI could assist health-care providers in avoiding errors and allow clinicians to focus on providing care and solving complex cases.
AI can also empower patients and communities to assume control of their own health care and better understand their evolving needs.
AI can enable resource-poor countries, where patients often have restricted access to health-care workers or medical professionals, to bridge gaps in access to health services.
The performance of AI depends (among other factors) on the nature, type and volume of data and associated information and the conditions under which such data were gathered.
Use of limited, low-quality, non-representative data in AI could perpetuate and
deepen prejudices and disparities in health care. Biased inferences, misleading data
analyses and poorly designed health applications and tools could be harmful. Predictive algorithms based on inadequate or inappropriate data can result in significant racial or ethnic bias.
AI technologies for health are increasingly distributed outside regulated health-care settings, including at the workplace, on social media and in the education system.
Key Messages:
Applications of Artificial Intelligence for Health
In Health care
Diagnosis and prediction-based diagnosis
In Low and Middle-Income Countries (LMIC), AI may be used to improve detection of tuberculosis in a support system for interpreting staining images or for scanning X-rays for signs of tuberculosis, COVID-19 or 27 other conditions.
Clinical care
Clinicians might use AI to integrate patient records during consultations, identify patients at risk and vulnerable groups, as an aid in difficult treatment decisions and
to catch clinical errors.
In LMIC, for example, AI could be used in the management of antiretroviral therapy by predicting resistance to HIV drugs and disease progression, to help physicians optimize therapy.
In Health research and drug development
Application of AI for health research
From electronic health records, AI that is accurately designed and trained with appropriate data can help to identify clinical best practices before the customary pathway of scientific publication, guideline development and clinical support tools.
AI can also assist in analyzing clinical practice patterns derived from electronic health records to develop new clinical practice models.
Uses of AI in drug development
AI could be used in drug discovery and throughout drug development to shorten the process and make it less expensive and more effective. AI was used to identify potential treatments for Ebola virus disease, although, as in all drug development, identification of a lead compound may not result in a safe, effective therapy.
In health systems management and planning
AI can be used to assist personnel in complex logistical tasks, such as optimization of the medical supply chain, to assume mundane, repetitive tasks or to support complex decision-making.
Some possible functions of AI for health systems management include:
- identifying and eliminating fraud or waste,
- scheduling patients,
- predicting which patients are unlikely to attend a scheduled appointment, and
- assisting in identification of staffing requirements.
In public health and public health surveillance
Health promotion
AI can be used for health promotion or to identify target populations or locations with “high-risk” behaviour and populations that would benefit from health communication and messaging (micro-targeting).
Disease prevention
AI tools can be used to identify bacterial contamination in water treatment plants, simplify detection and lower the costs.
Surveillance (including prediction-based surveillance) and emergency preparedness
AI has been used in public health surveillance for collecting evidence and using it to create mathematical models to make decisions.
Outbreak response
AI has been introduced to map the movements of individuals in order to approximate the effectiveness of government-mandated orders to remain in confinement, and, in some countries, AI technology has been used to identify individuals who should self-quarantine and be tested.
Key Ethical Principles for use of AI for Health
To limit the risks and maximize the opportunities intrinsic to the use of AI for health, WHO provides the following principles as the basis for AI regulation and governance:
Protecting human autonomy: In the context of health care, this means that humans should remain in control of health-care systems and medical decisions; privacy and confidentiality should be protected, and patients must give valid informed consent through appropriate legal frameworks for data protection.
Promoting human well-being and safety and the public interest. The designers of AI technologies should satisfy regulatory requirements for safety, accuracy and efficacy for well-defined use cases or indications. Measures of quality control in practice and quality improvement in the use of AI must be available.
Ensuring transparency, explainability and intelligibility. Transparency requires that sufficient information be published or documented before the design or deployment of an AI technology. Such information must be easily accessible and facilitate meaningful public consultation and debate on how the technology is designed and how it should or should not be used.
Fostering responsibility and accountability. Although AI technologies perform specific tasks, it is the responsibility of stakeholders to ensure that they are used under appropriate conditions and by appropriately trained people. Effective mechanisms should be available for questioning and for redress for individuals and groups that are adversely affected by decisions based on algorithms.
Ensuring inclusiveness and equity. Inclusiveness requires that AI for health be designed to encourage the widest possible equitable use and access, irrespective of age, sex, gender, income, race, ethnicity, sexual orientation, ability or other characteristics protected under human rights codes.
Promoting AI that is responsive and sustainable. Designers, developers and users should continuously and transparently assess AI applications during actual use to determine whether AI responds adequately and appropriately to expectations and requirements. AI systems should also be designed to minimize their environmental consequences and increase energy efficiency. Governments and companies should address anticipated disruptions in the workplace, including training for health-care workers to adapt to the use of AI systems, and potential job losses due to use of automated systems.
Useful Links:
Link to the related WHO news release:
Link to the WHO report on AI for Health (English) [PDF]:
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