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Special Committee on Artificial Intelligence in a Digital Age (AIDA)

Measures in Place

Key Questions

Key Conflicts

Key Actors

Key Terms

Why is this topic relevant? Explain the problem!

Key Actors and Stakeholders

Access

Ethics

Collaboration

Requirements

Responsibility

Key Questions

Privacy Issues and Implementation

Patient Harm

Lack of Transparency and Accountability

Algorithmic Bias

Key Conflicts

With the rapid advance and implementation of AI-powered innovation in medicine, certain conflicts emerge. At the heart of this lies the conflict between the desire to implement this technology as quickly as possible in order to save more lives and the need to thoroughly test and iterate on the experimental technology to analyse and eliminate any risks.

Collective Actions for positive Changes

EU Artificial Intelligence Act: The AI Act is a proposed European law on artificial intelligence that will regulate the use of AI in the EU. The law will classify AI systems according to their risk level, and impose different requirements on systems of varying risk levels. For example, unacceptable-risk AI systems will be banned, while high-risk AI systems must be registered in an EU database and assessed before being put on the market.

European Health Data Space allows individuals to control their health data, facilitates its use for healthcare delivery, research, innovation, and policy-making, and ensures secure data exchange within the EU.

Ethics guidelines for trustworthy AI: These guidelines were created by the EC-appointed High-Level Expert Group on AI in 2019. According to the guidelines, trustworthy AI should be lawful, ethical and robust. The guidelines further propose seven essential requirements for dependable AI systems, encompassing human agency and oversight, technical robustness and safety, privacy and data governance, transparency, diversity and fairness, societal and environmental well-being, and accountability,

The General Data Protection Regulation (GDPR): is a European Union regulation that enforces compulsory guidelines dictating how organisations and businesses should handle personal data in a manner that respects privacy. It goes further by disallowing exclusive automated decision-making and AI processing of health-related data.

Measures in Place

Tools like generative AI have extraordinary potential for accelerating the discovery of new drugs and treatments. They are playing a role in enhancing the accessibility, affordability, and efficiency of medical practices. Companies using generative AI have transitioned from uncovering new treatment targets to obtaining FDA drug approval within just 18 months, while spending less on what the usual development and approval process costs. Moreover, it has the potential to forecast how diseases might develop resistance to drugs, enabling personalised therapies.

Explanation and Relevance

The future of standard medical practice may arrive sooner than expected, where a patients could see a computer before seeing a doctor. Advances in artificial intelligence (AI) suggest that we could soon move past the days of misdiagnosis and treating disease symptoms rather than the root causes of diseases. Artificial intelligence in medicine employs machine learning models to analyse medical data and uncover insights to help improve health outcomes and patient experiences. AI algorithms and other AI-powered applications are being used to support medical professionals in clinical settings and ongoing research. Currently, the most common roles for AI in medical settings are clinical decision support and imaging analysis. Some benefits of AI-powered medicine include reducing the costs of care, increasing doctor-patient engagement and reducing diagnosis errors.

Chaired by: Olaf Scheepers (NL)

GPT MD: With AI starting to take a huge impact in the lives of European citizens, and with recent AI software being able to correctly identify up to 94% of lung cancer and being as proficient as trained pathologists in detecting internal haemorrhages, what should the EU do in order to nurture this endeavour in medicine while protecting its citizens from AI's potential harms?

SPECIAL COMMITTEE ON ARTIFICIAL INTELLIGENCE IN A DIGITAL AGE (AIDA)

Clinical decision support - A health information technology system or application that analyses medical data to help healthcare providers make decisions and improve patient care.

Algorithmic bias - Inaccuracies in a computer system that lead to unjust outcomes, favouring certain user groups arbitrarily over others. This bias arises when an algorithm generates consistently prejudiced results due to erroneous assumptions made during the machine learning process.

Algorithm - A procedure or set of instructions designed to solve a problem or perform a particular task. It is a fundamental concept in computer science used to develop computer programs, perform calculations, process data, and automate various processes. In the context of AI, algorithms typically require large sets of data to learn.

Imaging analysis - A process that uses algorithms and mathematical models to interpret and extract meaningful information from images.

Machine Learning - A subset of artificial intelligence that automatically enables a machine or system to learn and improve from experience. Instead of relying on explicit programming, machine learning utilises algorithms to analyse large amounts of data and learn from the insights and then make informed decisions.

Artificial Intelligence (AI) - The ability of a computer system to do tasks that typically require human intelligence and discernment such as understanding natural language, recognizing patterns, solving problems.

Key Terms