Artificial intelligence in healthcare

Artificial intelligence in healthcare

In recent years, the use of artificial intelligence (AI) in medicine and healthcare has been praised for the great promise it offers but has also been at the center of heated controversy.

This study offers an overview of how AI can benefit future healthcare, in particular increasing the efficiency of clinicians, improving medical diagnosis and treatment, and optimizing the allocation of human and technical resources.

The study proposes mitigation measures and policy options to minimize these risks and maximize the benefits of medical AI, including multi-stakeholder engagement through the AI production lifetime, increased transparency and traceability, in-depth clinical validation of AI tools, and AI training and education for both clinicians and citizens.

Specific applications of AI in medicine and healthcare

This study first outlines the potential for AI in medicine to address pressing issues, in particular, the aging population and the rise of chronic diseases, a lack of health personnel, inefficiency of health systems, lack of sustainability, and health inequities.

The report also details the different fields in which biomedical AI could make the most significant contributions:

  • Clinical practice
  • Biomedical research
  • Public health
  • Health administration.

In the realm of clinical practice, the report goes into further detail concerning specific contributions – both realized and potential – to particular medical areas such as radiology, cardiology, digital pathology, emergency medicine, surgery, medical risk, and disease prediction, adaptive interventions home care, and mental health.

In biomedical research, the report details the potential contributions of AI to clinical research, drug discovery, clinical trials, and personalized medicine. Lastly, the report presents the potential contributions of AI at the public health level as well as to global health.

Risks of AI in healthcare
  • This study identified and clarifies seven main risks of AI in medicine and healthcare:
  • Patient harm due to AI errors
  • The misuse of medical AI tools
  • Bias in AI and the perpetuation of existing inequities
  • Lack of transparency
  • Privacy and security issues
  • Gaps in accountability,
  • Obstacles in implementation.

Each section, as summarized below, not only describes the risk at hand but also proposes potential mitigation measures.



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