From Brain Organoids to Organoid Intelligence. Benefits and Ethical-Moral Framework

Organoid intelligence (OI) is an emerging field in biological computing that integrates brain organoids with external interfaces capable of receiving and transmitting signals. This article aims to provide an overview of the current state of organoid intelligence and its ethical and moral frameworks. A fundamental question is posed: can brain organoids connected to a computer interface be said to be endowed with consciousness? If the answer is yes, brain organoids connected to an interface would be defined as conscious entities. If the above were true, would the scientific community have an ethical regulatory framework for research? Recent studies show a lack of definition regarding “consciousness”. Consequently, the detection and measurement of consciousness in more advanced brain organoids is compromised, as there is no clear definition of what is being measured. Responsible communication that adheres to scientific reality is important to avoid misconceptions. The use of measuring intelligence rather than consciousness is recommended, as intelligence is a better-defined concept. At present, there is no universally accepted definition of consciousness, which makes the ethical evaluation of brain organoids particularly complex. The prevailing consensus among researchers is that brain organoids should currently be regarded as non-conscious entities. However, if future developments were to demonstrate evidence of sentience or consciousness in more advanced organoids, the scientific community would face a significant challenge, as there is not globally standardized ethical or regulatory framework prepared to address such scenarios. Defines a conceptual framework for organoid intelligence linking biology, computation, and ethics. Synthesizes evidence on brain organoids as human-relevant models that reduce animal use (3Rs). Maps current regulatory gaps and proposes threshold-based oversight for potential sentience. Contrasts OI with AI on energy footprint, outlining sustainability advantages and open challenges. Prioritizes R&D: robust I/O interfaces, neurophys metrics, data standards, and reproducibility.