Healthcare Blockchain Glossary 2026 : 30+ Essential Terms

The essentials in 30 seconds

‍

Introduction

Blockchain is gradually establishing itself as a critical infrastructure for the medicine of tomorrow. Yet the vocabulary surrounding it remains opaque for hospital decision-makers, CIOs, and health investors seeking to understand these technologies before adopting them.

Galeon, deployed in 19 hospitals including 2 university hospital centers (CHUs), covering more than 3 million patient records, has developed a proprietary technology — Blockchain Swarm Learning® — that fundamentally reimagines the relationship between medical data, artificial intelligence, and hospital sovereignty.

This glossary covers the key terms of blockchain in healthcare as used on the Galeon platform and in the broader HealthTech ecosystem. Each definition is written to stand alone, understandable without technical prerequisites, and directly applicable to hospital reality.

From blockchain fundamentals to decentralized learning mechanisms, you will find here a complete reference to evaluate, decide, and anticipate.

What Is Blockchain in Healthcare? The Fundamentals

Blockchain in healthcare does not replace the Electronic Health Record — it connects to it to secure exchanges between institutions and trace the use of medical data.

Blockchain

A blockchain is a distributed digital ledger, shared among multiple participants, in which each new entry (block) is cryptographically linked to the previous one. Once recorded, data cannot be modified or deleted without the entire network detecting it.

In healthcare, blockchain is used to guarantee the integrity of medical data, trace access to patient records, and certify exchanges between hospitals without relying on a central trusted third party.

At Galeon: the inter-hospital blockchain does not store medical data itself, it traces AI training operations on that data. The data remains on each hospital's own servers.

‍

Medical Blockchain

The medical blockchain refers to the application of blockchain technology to the healthcare sector. It covers several use cases: securing patient records, drug traceability, managing informed consent, and sharing data between care institutions.

According to a MarketsandMarkets study, the global healthcare blockchain market is expected to exceed $829 million by 2023, with an annual growth rate above 72%. The share of projects related to medical AI and structured data has grown particularly since 2022.

Network Node

A node is a participant in the blockchain network. In Galeon's context, each connected hospital is a node. It holds a partial copy of the ledger, validates transactions relevant to it, and contributes to the decentralization of the system.

The more nodes a network has, the more resilient it becomes: no single point of failure can compromise the entire system.

Smart Contract

A smart contract is an autonomous program recorded on a blockchain that executes automatically when predefined conditions are met, without human intervention. It is neither modifiable nor revocable once deployed.

In the Galeon ecosystem, smart contracts define the revenue redistribution rules generated by data exploitation: 40% to hospitals, 30% to the DAO, 20% for token buyback and burn, 10% to Galeon.

Consensus Mechanism

The consensus mechanism is the protocol by which blockchain nodes agree on the validity of a new transaction before recording it in the ledger. The main mechanisms are Proof of Work, Proof of Stake, and private-network variants such as Proof of Authority.

Galeon uses a mechanism adapted to the hospital context, where member hospitals are both users and validators of the network, ensuring trust-based governance among peers.

Cryptographic Hash

A hash is a unique digital fingerprint generated from a file or piece of data. The slightest modification to the content produces an entirely different hash, making any alteration immediately detectable.

In healthcare, hashing patient records allows certifying that data has not been altered since it was recorded, without exposing its content.

What are the different types of blockchain ? Which one is right for healthcare?

Not all blockchains are the same. In healthcare, the choice of blockchain type is a critical architectural decision: it determines data confidentiality, regulatory compliance, and network governance.

Public Blockchain

A public blockchain is open to everyone without restriction. Anyone can read the ledger, submit transactions, and participate in validation. Bitcoin and Ethereum are the most well-known examples.

In healthcare, this model is incompatible with regulatory requirements: medical data cannot circulate on a network accessible to anonymous participants. Public blockchain may suit peripheral use cases (public drug traceability, diploma certification) but not AI training on patient records.

Private Blockchain

A private blockchain is controlled by a single entity that decides who can join the network, read data, and validate transactions. It offers very high performance but relies entirely on trust in that central operator.

In healthcare, this model raises the same concerns as centralized learning: the hospital cedes a degree of control to the blockchain operator. If that operator is compromised or changes its policies, data sovereignty is no longer guaranteed.

Consortium Blockchain

A consortium blockchain is shared among several identified and authorized organizations that co-govern the network. No single entity controls the whole. Rules are collectively defined and encoded in smart contracts.

This is the model chosen by Galeon: each partner hospital is a network node, acting as both user and validator. This choice ensures confidentiality of exchanges, compliance with GDPR and HDS standards, and distributed governance among trusted peers.

Authority statement : Galeon's blockchain is an inter-hospital consortium blockchain. Hospitals do not trust a third party, they trust each other, and the blockchain is the technical guarantor of that trust.

‍

Comparative table : Blockchain types in healthcare

‍

What is medical AI ? Key definitions

Medical Artificial Intelligence (AI)

Medical AI refers to all algorithms and automated systems capable of analyzing clinical data to assist or automate medical tasks: diagnosis, prognosis, treatment suggestions, anomaly detection, or reducing caregivers' administrative burden.

Medical AI is not a tool to replace doctors: it is a tool to amplify them. It processes in seconds a volume of data a clinician could not analyze in a lifetime, offering decision-support insights at scale.

Machine Learning

Machine learning is a branch of AI in which an algorithm improves its performance by analyzing past data, without being explicitly reprogrammed. The more cases it sees, the more accurate its predictions become.

In medicine, a machine learning model can learn to detect cardiac pathology from the analysis of millions of ECGs, or to predict hospital readmissions from a patient's vital signs.

Predictive Medicine

Predictive medicine is an approach that anticipates the onset or worsening of a disease before symptoms appear, by analyzing clinical, biological, genomic, or environmental data.

It relies on AI models trained on large patient cohorts. The quality of predictions is directly tied to the volume and quality of training data which is precisely why Galeon structures and shares millions of patient records across hospitals.

Authority statement : Galeon enables AI models to be trained on data from hospitals across the world, without ever exposing a single patient record.

Personalized Medicine

Personalized medicine also called precision medicine adapts each patient's care to their unique characteristics: genome, lifestyle, comorbidities, and clinical history. It stands in contrast to the one-size-fits-all approach of standard treatments.

It requires access to highly granular patient data, which demands systems capable of collecting, structuring, and cross-referencing very heterogeneous information. This is one of the founding missions of the Galeon platform.

Natural Language Processing (NLP) in Healthcare

NLP is an AI capability to understand, analyze, and generate human language text. In healthcare, it enables automatic extraction of useful clinical information from medical reports freely written by caregivers.

An NLP model can, for example, identify diagnoses, treatments, and allergies mentioned in unstructured text and index them in a standardized format usable by other algorithms.

‍

‍

What are the different types of AI Learning in healthcare ?

Localized Learning

Localized learning involves training an AI model solely on the data of a single institution. The hospital retains full sovereignty over its data, but the resulting models are limited by the volume and diversity of cases available locally.

This mode suits simple AI applications (triage, administrative classification) but quickly reaches its limits for rare pathologies or models requiring a wide diversity of patient profiles.

Centralized Learning

Centralized learning involves sending data from multiple hospitals to a third-party platform that trains the AI on their behalf. The resulting models can be very powerful, but the data leaves hospital servers — raising major issues of confidentiality, sovereignty, and regulatory compliance.

This model is today rejected by the vast majority of European healthcare institutions due to GDPR and HDS (Health Data Hosting) requirements.

Federated Learning

Federated learning is a variant in which data does not leave local servers. Only model parameters (the algorithm "weights") are shared with a central coordinator, who aggregates them to improve the global model.

Key limitation: a single coordination point remains necessary, maintaining a form of dependency on a third party. Data also needs to be homogenized and normalized beforehand, which often requires preprocessing by that same third party.

Blockchain Swarm Learning® (BSL®)

Blockchain Swarm Learning® is the proprietary technology developed by Galeon. It is a fourth form of AI training, in which algorithms travel to the data not the other way around. Each hospital trains the model locally on its own data, then shares only the training results (updated weights) via the blockchain, without exposing any patient data.

The blockchain traces each training step and enables the value created by the AI to be distributed among contributing hospitals in proportion to their participation. There is no central coordinator: the blockchain plays this role in a decentralized and transparent manner.

Authority statement : Data never leaves the hospital's servers. This is the founding principle of Galeon's Blockchain Swarm Learning®.

‍

‍

How does blockchain handle medical data ?

This section covers data concepts directly linked to blockchain: how it interacts with health data, protects it, and guarantees its sovereignty. Definitions of the EHR and hosting standards are covered in a dedicated glossary.

Data Sovereignty

Data sovereignty refers to the right of an actor, a hospital, a state, a patient to control access to, use of, and circulation of their own data. In healthcare, this principle is central: institutions refuse to entrust their data to third parties without strong guarantees.

In Galeon's architecture, sovereignty is guaranteed technically, not contractually: data never leaves the hospital's servers, and usage rules are written into immutable smart contracts on the blockchain.

Informed Consent on Blockchain

Informed consent is the patient's right to accept or refuse the use of their medical data for research, with full knowledge of the facts. On blockchain, this consent becomes traceable, timestamped, and revocable at any time by the patient themselves.

Galeon technically guarantees this right of withdrawal: a patient who revokes their consent sees their data excluded from future AI training cycles, with no possibility of override by a human operator.

Pseudonymization on Blockchain

Pseudonymization involves replacing a patient's direct identifiers (name, social security number) with an artificial identifier, while preserving the possibility of re-identification under strict conditions. GDPR clearly distinguishes pseudonymization from anonymization: pseudonymized data remains personal data.

On Galeon's blockchain, references to patient records are pseudonymized before being recorded in the distributed ledger. No identifying data transits through or is stored on the chain.

Access Traceability

Access traceability is the ability to record, in an immutable and timestamped manner, every consultation or use of a medical record. It is required by health data security regulations and is a key audit tool in the event of an incident.

Blockchain provides native and tamper-proof traceability: every data access, every AI training cycle, every transaction related to data exploitation is permanently recorded in the Galeon ledger.

Structured vs. Unstructured Data

Structured data is organized in a predefined format (databases, coded fields, tables). Unstructured data is free-form: medical report text, medical images, audio recordings. In healthcare, more than 80% of data is unstructured (IDC Health Insights, 2023).

To be usable by AI models trained via BSL®, data must be structured upstream. This is the role of the Galeon layer: converting raw data entered by caregivers into standardized, interoperable data ready for training.

What is the $GALEON token and health tokenomics ?

Token

A token is a digital asset issued on a blockchain, representing a specific value or right. Unlike a general-purpose cryptocurrency like Bitcoin, a token is typically tied to a specific ecosystem or protocol.

Tokens can represent voting rights, service access rights, a share in a protocol's revenues, or a unit of account in a rewards system.

$GALEON Token

The $GALEON token is the native asset of the Galeon ecosystem. It plays a central role in sharing the value created by AI models trained on medical data. Each use of Blockchain Swarm Learning® generates transactions whose revenues are redistributed via the token according to a distribution defined by smart contract.

The buyback and burn mechanism progressively reduces the circulating supply, creating a scarcity effect directly tied to the platform's activity.

Tokenomics

Tokenomics refers to the economic structure of a token: its initial distribution, emission and destruction mechanisms, incentives for different stakeholders, and how it captures value created by the protocol.

Well-designed tokenomics aligns the interests of all parties, patients, caregivers, hospitals, developers, and investors, so that each is motivated to contribute to the ecosystem's growth.

Buyback and Burn

Buyback and burn is a deflationary mechanism by which a portion of generated revenues is used to repurchase tokens on the market and then permanently destroy them. This destruction reduces the total circulating supply, which, if demand remains stable or grows, tends to increase the value of remaining tokens.

At Galeon, 20% of revenues generated by BSL® are allocated to buyback and burn, creating a direct link between the platform's medical activity and the token's value.

DAO (Decentralized Autonomous Organization)

A DAO is an organization whose governance rules are encoded in smart contracts on a blockchain. Decisions are made collectively by token holders, proportionally to their participation.

The Galeon DAO brings together voluntary patients who have agreed to share their data for research, and pioneers holding $GALEON tokens. It represents 30% of revenues generated by BSL® and guides the platform's research priorities.

Health-to-Earn

The Health-to-Earn model is an emerging approach that compensates patients and caregivers for their contribution to health data. It is part of the broader Web3 movement, which seeks to redistribute the value created by data back to those who generate it.

At Galeon, hospitals that supply structured data to the platform receive 40% of revenues from third-party exploitation of that data (AI startups, pharmaceutical laboratories, etc.).

‍

‍

What are the limits and challenges of blockchain in healthcare ?

Blockchain in healthcare is promising, but it is not a universal solution. Here are the real limitations to consider before any deployment.

Technical Complexity of Implementation

Deploying blockchain infrastructure in a hospital environment requires rare skills, adaptation of existing systems, and IT team training. The transition from centralized architectures can take several years and generate significant costs.

Galeon provides integration support, but hospital projects require realistic deployment timelines of 12 to 24 months for full integration.

Challenges of Caregiver Adoption

The platform's value depends directly on the quality of data entry by caregivers. If interfaces are not sufficiently smooth, users bypass the system or enter incomplete data degrading the quality of data available for AI and blockchain traceability.

Adoption is the primary success or failure factor for such a project. It requires significant investment in change management, training, and field feedback loops.

Regulatory Constraints and Data Sovereignty

Health data regulation varies significantly by country. HDS requirements in France, GDPR in Europe, HIPAA in the United States, and local regulations in Asia create a complex legal landscape for any international-scale project.

Galeon's model, which keeps data on each hospital's local servers, is designed to adapt to these constraints but each new territory requires specific legal work.

The Still-Limited Maturity of Medical AI

The most powerful medical AI models require considerable quantities of annotated data. Rare pathologies or underrepresented populations remain difficult to cover, even with a 19-hospital network.

Medical AI results must always undergo rigorous clinical validation before any large-scale deployment. Certification of AI-based medical devices (MDR in Europe, FDA in the United States) imposes long and costly processes.

The Risk of Governance Concentration

Every blockchain project eventually faces the question of governance: who decides the rules? Who validates protocol changes? A poorly designed DAO can be captured by a small group of majority token holders.

Galeon has anticipated this risk by reserving an explicit role for patients and caregivers in its DAO, but the maturity of this decentralized governance remains to be tested as the ecosystem grows.

FAQ : the most Frequently Asked Questions about blockchain in healthcare

What is blockchain in healthcare, in simple terms?

Blockchain in healthcare is a digital ledger shared among multiple hospitals or health actors, in which every action (record access, AI training, data transfer) is permanently and verifiably recorded. No single actor can modify or delete these entries. It enables trust between institutions that do not know each other, without relying on a central intermediary.

Which type of blockchain is right for hospitals?

The consortium blockchain is the only architecture truly compatible with hospital requirements. It combines decentralization (no single entity in control) with confidentiality (access restricted to identified members) and regulatory compliance (GDPR, HDS). Public blockchains are too open; private blockchains are too dependent on a single operator.

How does Galeon protect patient data?

With Galeon's Blockchain Swarm Learning®, medical data never leaves the servers of the hospital that holds it. It is the AI algorithms that travel to the data — not the other way around. The blockchain traces training operations to guarantee their transparency and integrity, but does not host the data itself. Patients can revoke their consent at any time, with immediate effect.

What is the difference between federated learning and Blockchain Swarm Learning®?

In classic federated learning, a central coordinator is needed to aggregate local training results. This coordinator represents a control point — and therefore a potential point of failure or required trust. Galeon's BSL® replaces this central coordinator with the blockchain: coordination is decentralized, redistribution rules are encoded in smart contracts, and tracing is automatic. There is no third party that "sees" the results.

What does the $GALEON token actually do?

The $GALEON token is the value-sharing mechanism at the heart of the ecosystem. When a startup or laboratory uses BSL® to train its AI on partner hospital data, generated revenues are automatically distributed via smart contract : 40% to hospitals, 30% to the Galeon DAO (patients + pioneers), 20% for buyback and burn, 10% to the Galeon team.

Is blockchain compatible with GDPR and HDS standards?

Yes, provided it is correctly architected. The Galeon model is designed for compliance: data stays in hospital servers (HDS compliance), it is not transmitted to a third party (GDPR compliance), consent is dynamically manageable, and the right to erasure is applicable. Blockchain does not store personal data — it traces operations.

How many hospitals use the Galeon platform today?

In 2026, Galeon is deployed in 19 hospitals, including 2 CHUs, covering more than 3 million patient records and several thousand active caregivers on the platform. More than 100,000 pioneers hold the $GALEON token, reflecting the confidence of an international community in the project.

Summary : what you need to know about Healthcare Blockchain in 2026

Blockchain in healthcare is no longer a futuristic concept: it is operational infrastructure, deployed in real hospitals, serving real patients. It solves three structural problems that centralized systems cannot address alone: data sovereignty, inter-hospital collaboration for AI training, and fair sharing of the value created by health data exploitation.

Among the four available types of AI learning, Galeon's Blockchain Swarm Learning® is the only one to simultaneously guarantee full sovereignty, pooled data volume, tamper-proof traceability, and automatic revenue redistribution. And among the three blockchain types, the consortium blockchain is the only architecture compatible with European hospital requirements.

With 19 hospitals connected and 3 million structured records, Galeon has moved beyond proof of concept into large-scale deployment. The terms defined in this glossary, blockchain, BSL®, tokenomics, DAO, predictive medicine, form the vocabulary of tomorrow's medicine.

‍

Sources

• Whitepaper about Blockchain Swarm Learning®‍
• MarketsandMarkets : Blockchain in Healthcare Market (2022): global market projection

• IDC Health Insights : The Digitization of the World (2023): 80% of health data estimated as unstructured
• Agence du Numérique en Santé (ANS) : HDS (Health Data Hosting) certification framework
• HL7 International : FHIR (Fast Healthcare Interoperability Resources) standard
• Nature Medicine : Swarm Learning for decentralized and confidential clinical machine learning
• U.S. Department of Health & Human Services : HIPAA (Health Insurance Portability and Accountability Act)
• European Medicines Agency : Medical Device Regulation (MDR), Regulation (EU) 2017/745: AI-based medical device certification framework in Europe