Artificial Intelligence - Applications in Healthcare Delivery
Artificial Intelligence in Medicine
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Artificial Intelligence in Clinical care Delivery:
Artificial Intelligence in Healthcare
Advances in artificial intelligence (AI) have the potential to translate into many applications in healthcare. Different areas will be affected, including:
Research: AI can identify correlations or patterns in large datasets of different types (e.g., clinical data, lifestyle habits, genetic profile, socioeconomic conditions, physical environment, etc.). This could, for example, allow the discovery of risk factors for diseases or, on the contrary, determinants of health.
Care organization: AI could support the planning of care services, promote optimal use of resources (budget, equipment, nursing and support staff, emergency triage, etc.) and improve institutions' interactions with patients.
Mobile applications and connected objects: Some mobile applications and connected objects use AI. In particular, these devices can continuously monitor and evaluate certain physiological parameters of the patient. They can support prevention, diagnosis and disease management.
Clinical care delivery: AI can support or replace healthcare professionals in certain tasks such as diagnosis, prognosis assessment, treatment selection and precision surgery.
In order to work in complementarity with other organizations, working committees and partners, the Commission has decided to focus on the ethical issues raised by AI in the delivery of clinical care (diagnosis, prediction, therapeutic choices). It will pay particular attention to the supervision of medical instruments and devices integrating AI.
Ethical issues
Applications of AI in the delivery of care involve various ethical issues that need to be assessed and managed. Some examples include:
Quality of AI systems:
Reliability and safety: AI systems can make errors that have the potential to cause serious harm, especially when it comes to supporting the physician in diagnosing and choosing a treatment option. Therefore, the reliability of AI systems must be assessed.
Neutrality: AI applications can reproduce or reinforce existing biases, or introduce new biases. These biases can be introduced at the programming stage or when machines are trained with large amounts of data. For example, when machines are trained with historical data, the outputs of AI systems will tend to conform to past outputs.
Autonomy:
Patient Autonomy: Some AI applications, such as mobile health apps and connected objects, can support patient autonomy or home care for vulnerable people. On the contrary, the opacity of some AI systems that provide treatment options limits the patient's ability to give informed consent.
Professional autonomy: AI systems that make diagnoses and recommend treatment protocols can reduce the autonomy of healthcare professionals.
The patient-physician relationship:
Some observers are concerned that the use of AI systems will reduce the patient to an aggregate of data and degrade the quality of the patient-physician relationship. Human listening and empathy are an essential component of this relationship in that they increase trust and quality of care. On the contrary, other experts believe that AI assistance will free healthcare professionals from some time-consuming tasks and allow them to devote more of their time to the relationship with the patient.
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