1. Introduction

A Digital Twin Organism (DTO) is a computer-generated virtual representation of a living biological organism. It uses biological, physiological, genetic, medical, environmental and behavioural information to reproduce or simulate the characteristics and functioning of an actual organism.

A digital twin may represent:

A human being, including organs, tissues and physiological systems.

An animal used in scientific or pharmaceutical research.

A plant or genetically modified organism.

A microorganism, such as bacteria or viruses.

An entire biological system or ecosystem.

For example, researchers may create a digital twin of a human heart to simulate cardiac disease, predict the effects of medicines and assess possible treatment outcomes without initially conducting every experiment on the actual patient.

The development of digital twin organisms raises important legal questions concerning regulatory classification, human dignity, biological data protection, medical-device approval, research ethics, intellectual property, liability and biosafety.

Central legal question: Should a digital twin organism be regulated as software, a medical device, a scientific research model, a biological product or an independent legal entity?

The answer depends on its purpose, design, intended use and potential risks. A digital twin does not automatically acquire the legal personality of the biological organism it represents.

2. Meaning and Legal Nature of a Digital Twin Organism

A digital twin organism is not necessarily a simple computer model. It may continuously receive information from sensors, medical devices, genomic databases and laboratory experiments.

Its main characteristics include:

Biological representation: It represents the structure or functioning of a real organism.

Data integration: It incorporates biological, genetic and physiological information.

Predictive simulation: It predicts possible biological responses and disease developments.

Continuous updating: It can be updated using new information from the actual organism.

Experimental functionality: It allows researchers to test hypothetical treatments, interventions and biological changes.

From a regulatory perspective, the most important distinction is between a digital model used exclusively for scientific research and one used to make decisions affecting an actual patient, animal or biological system.

For example, a digital heart model used for academic research may be treated differently from software used to recommend cardiac surgery.

3. Regulatory Classification of Digital Twin Organisms

There is currently no universally accepted, standalone legal category called a Digital Twin Organism. Its regulatory status is generally determined through existing laws governing software, medical devices, biological research, privacy, intellectual property and scientific experimentation.

The following classifications are particularly important.

Important distinction: A digital twin that merely stores biological information may not be a medical device. A digital twin that analyses patient-specific data to diagnose disease, predict deterioration or recommend treatment may qualify as regulated medical-device software.

4. Regulatory Framework in Australia

Australia provides an important framework for examining the legal status of digital twin organisms.

A. Therapeutic Goods Act 1989 (Cth)

The Therapeutic Goods Act 1989 is central to regulating digital twins used for medical purposes.

Section 41BD establishes the statutory definition of a medical device.

A digital twin may fall within this framework if its intended purpose involves:

Diagnosis of disease.

Prediction of disease progression.

Monitoring of physiological conditions.

Treatment recommendations.

Investigation of human anatomy.

Investigation or modification of physiological processes.

For example, a digital twin of a patient's cardiovascular system that predicts the risk of heart failure and supports clinical treatment decisions may qualify as software as a medical device (SaMD).

Where regulated, the product generally requires inclusion in the Australian Register of Therapeutic Goods (ARTG) before supply, unless an exclusion or exemption applies.

The TGA's guidance published on 24 February 2026 explains that intended purpose, functionality, risk classification and software updates are important in determining regulatory status.

Therapeutic Goods Administration (TGA)+1

B. Therapeutic Goods (Medical Devices) Regulations 2002

These Regulations establish classification and conformity-assessment requirements.

A digital twin used in clinical decision-making must be assessed according to its intended purpose and potential risk.

Relevant compliance considerations include:

Software validation.

Accuracy and reliability of predictions.

Cybersecurity.

Data integrity.

Risk-management procedures.

Technical documentation.

Version control and software updates.

Post-market monitoring.

Schedule 1 contains the Essential Principles concerning medical-device safety and performance, including requirements relevant to software security, data quality and privacy.

Federal Register of Legislation+1

C. Privacy Act 1988 (Cth)

Digital twin organisms may process highly sensitive personal information, particularly when they contain:

Genetic information.

Medical histories.

Biometric information.

Physiological measurements.

Reproductive information.

Disease-related predictions.

The Privacy Act 1988 and Australian Privacy Principles (APPs) may regulate the collection, use, disclosure and security of personal information.

Particular attention should be given to APP 3 (collection), APP 6 (use and disclosure), APP 8 (cross-border disclosure) and APP 11 (security).

A company cannot assume that creating a virtual biological representation gives it unrestricted ownership or use rights over the underlying personal information.

D. Gene Technology Act 2000 (Cth)

The Gene Technology Act 2000 becomes relevant where digital twin technology is connected with dealings involving genetically modified organisms (GMOs).

For example, a digital model might simulate the consequences of changing the genetic structure of a microorganism or plant.

However, an ordinary computational simulation does not automatically become a genetically modified organism merely because it represents genetic modifications.

The actual biological dealings, if any, must be assessed under the applicable gene technology framework.

E. National Statement on Ethical Conduct in Human Research

Digital twins developed from human participants' biological information may also involve human research ethics requirements.

Researchers should consider:

Informed consent.

Secondary use of biological data.

Identifiability and re-identification risks.

Research involving vulnerable participants.

Data retention and destruction.

Independent ethical review where required.

A digital twin created from a patient's medical records should not automatically be treated as authorised for every future commercial or experimental purpose.

5. Important Judicial Decisions and Case Laws

There is no established leading judgment specifically recognising a digital twin organism as an independent legal entity. Nevertheless, several judicial decisions provide important principles for determining its regulatory status.

Case 1. Snitem and Philips France v Premier ministre (2017)

Court of Justice of the European Union · Case C-329/16 · ECLI:EU:C:2017:947

Facts: The dispute concerned software used to assist doctors in prescribing medicines. The software analysed patient-specific information to identify possible contraindications, drug interactions and excessive doses.

Judgment: The Court held that software performing such medical functions could qualify as a medical device even though it did not act directly on the human body.

Legal principle: Regulatory classification depends substantially on the intended medical purpose and functionality of the software.

Application to digital twin organisms: A digital twin that analyses individual biological information to predict disease or recommend treatment may qualify as medical-device software. Its virtual nature does not automatically exempt it from regulation.

EUR-Lex+1

Case 2. Brain Products GmbH v BioSemi VOF (2012)

Court of Justice of the European Union · Case C-219/11

Facts: The dispute concerned whether a product used in a medical environment automatically qualified as a medical device.

Judgment: The Court emphasised that the manufacturer's specifically intended medical purpose is essential. Merely being used in a healthcare environment is insufficient.

Legal principle: Medical-device classification depends on intended purpose rather than the location or environment in which software is used.

Application: A digital twin developed solely for biological education or general scientific simulation will not automatically become a medical device. Its intended function must be examined.

Case 3. D'Arcy v Myriad Genetics Inc (2015)

High Court of Australia · [2015] HCA 35 · (2015) 258 CLR 334

Facts: The case concerned patent claims over isolated genetic material associated with susceptibility to breast and ovarian cancer.

Judgment: The High Court held that the claimed isolated nucleic acid was not patentable subject matter in the circumstances.

Legal principle: Discoveries and naturally occurring biological information cannot automatically be transformed into patentable inventions merely through technical isolation or manipulation.

Application: Developers of digital twin organisms must distinguish between:

Naturally occurring biological information.

Raw genomic data.

Computational methods.

Novel software architecture.

Technical inventions involving biological simulations.

A digital representation of a naturally occurring organism does not automatically create an exclusive patent right over that organism or its biological characteristics.

High Court of Australia

Case 4. Rogers v Whitaker (1992)

High Court of Australia · (1992) 175 CLR 479

Facts: The case concerned a medical practitioner's duty to disclose material risks associated with proposed treatment.

Judgment: The High Court recognised the importance of patient autonomy and the disclosure of material medical risks.

Legal principle: Medical professionals must respect patients' rights to make informed decisions about medical treatment.

Application: If a doctor relies on a digital twin to recommend surgery or another intervention, the patient should receive an appropriate explanation of material risks, limitations and uncertainties associated with that recommendation.

A computer-generated prediction should not replace meaningful informed consent.

Case 5. Wallace v Kam (2013)

High Court of Australia · [2013] HCA 19 · (2013) 250 CLR 375

Facts: The case concerned medical negligence and the relationship between failure to warn about risks and the harm suffered by a patient.

Judgment: The Court examined the scope of liability and the causal relationship between the alleged breach and the injury.

Legal principle: Establishing negligence requires more than identifying a breach; the relevant legal causation requirements must also be satisfied.

Application: If a digital twin produces an incorrect prediction and a patient suffers injury, liability requires examination of the relevant duties, breach, causation and scope of liability. Responsibility may involve the clinician, manufacturer, developer or healthcare institution, depending on the circumstances.

Case 6. Moore v Regents of the University of California (1990)

Supreme Court of California · 51 Cal. 3d 120 · 793 P.2d 479

Facts: John Moore alleged that biological materials removed during medical treatment were used to develop a commercially valuable cell line without his informed consent.

Judgment: The Court rejected his conversion claim over the excised cells but allowed claims relating to the physician's disclosure obligations to proceed.

Legal principle: Biological materials, informed consent and commercial exploitation raise distinct legal questions. A patient's interests in medical decision-making cannot simply be equated with ownership of every resulting research product.

Application: Where digital twins are generated using human biological samples, developers must carefully consider consent, disclosure, privacy and commercial-use permissions. This judgment is from the United States and is persuasive comparative material, not binding Australian authority.

Case 7. State v Loomis (2016)

Wisconsin Supreme Court, United States · 881 N.W.2d 749

Facts: The case concerned the use of the proprietary COMPAS algorithm in criminal sentencing risk assessment.

Judgment: The Court permitted limited consideration of the algorithmic assessment, subject to important warnings and restrictions.

Legal principle: Algorithmic predictions must be approached cautiously, particularly where their methodology is opaque and individual rights are affected.

Application: A digital twin's predictions should not be treated as infallible scientific facts. Clinical decisions should consider model limitations, validation evidence, uncertainty and independent professional judgment.

6. Legal Status of Digital Twin Organisms as Independent Entities

One of the most significant unresolved legal questions is whether a digital twin organism can possess independent legal personality.

Traditionally, legal systems recognise natural persons and legally constituted entities such as corporations. A computational representation of a human, animal or microorganism does not automatically fall into either category.

Three possible legal approaches exist.

A. Digital twin as property or intellectual property

A digital twin may contain software, databases, computational models and proprietary algorithms.

Depending on the applicable law, developers may obtain copyright or patent protection for eligible components.

However, such protection does not necessarily confer ownership over the real organism, its natural biological characteristics or the personal information used to construct the model.

B. Digital twin as regulated software

This is currently the most practical regulatory approach.

Where the twin performs medical functions, it may be regulated as medical-device software. Where it is used only for scientific research, other research, privacy and institutional requirements may apply.

C. Digital twin as an independent legal personality

Some scholars may argue that highly sophisticated digital biological models should eventually receive a special legal status.

However, there is presently no general legal recognition of digital twin organisms as independent legal persons in Australia, India, the United States or the European Union.

A digital twin cannot independently acquire human rights, medical consent rights or biological ownership merely because it accurately reproduces the characteristics of a living organism.

7. Liability for Digital Twin Organism Errors

Digital twin technology creates complicated liability questions when simulated biological predictions differ from actual biological outcomes.

Consider a situation in which a hospital uses a digital twin to predict how a patient's body will respond to a particular medication. The model incorrectly predicts a safe dosage, resulting in serious injury.

Potentially responsible parties include:

The relevant legal claims may arise under negligence, contract, consumer protection, privacy and product liability principles.

An important distinction is that an inaccurate prediction does not automatically establish negligence. Courts must examine the applicable standard of care, foreseeability, causation and relevant statutory requirements.

8. Scientific Validation and Evidentiary Standards

A digital twin organism should not be accepted as scientifically reliable merely because it produces sophisticated visualisations or highly detailed predictions.

Its reliability requires appropriate scientific validation.

Important regulatory considerations include:

Biological accuracy.

Reproducibility of experimental results.

Validation against real biological observations.

Transparency of assumptions.

Identification of model limitations.

Independent scientific assessment.

Documentation of software modifications.

Continuous monitoring of prediction performance.

For example, a digital twin of the human liver should demonstrate that its predictions are sufficiently reliable for the particular intended application.

A model validated for academic research should not automatically be considered validated for clinical diagnosis or treatment.

9. Intellectual Property Rights and Ownership

Digital twin organisms create several overlapping intellectual property questions.

Copyright: Copyright may protect original software code, databases where applicable, documentation and graphical representations.

Patents: Eligible novel technical inventions may receive patent protection, subject to statutory requirements.

Trade secrets: Confidential algorithms, model architecture and proprietary datasets may qualify for trade-secret protection.

Biological materials: Ownership and control of physical biological samples must be considered separately from rights in the digital model.

A particularly difficult issue arises when a digital twin is created using information supplied by a patient but developed commercially by a private company.

The company may own or control certain software components, but this does not automatically give it unrestricted rights over the patient's personal or genetic information.

10. Biosafety and Biosecurity Regulation

Digital twin organisms can be used to simulate biological processes involving pathogens, genetic modifications and infectious diseases.

Such technology may create regulatory concerns when simulations are connected with actual laboratory experiments or biological materials.

Potential risks include:

Unsafe experimental predictions.

Inaccurate pathogen behaviour simulations.

Misuse of biological information.

Unauthorised access to sensitive research data.

Cyberattacks against biological research infrastructure.

Improper reliance on simulated safety assessments.

In Australia, the Gene Technology Act 2000, applicable biosecurity legislation and institutional biosafety requirements may become relevant depending on the actual biological activities involved.

A computational simulation is not automatically equivalent to handling a dangerous pathogen. Nevertheless, its connection with real-world biological experimentation must be assessed carefully.

11. International Regulatory Developments

Digital twin organisms may operate across national borders because their software, data and computing infrastructure can be located in different countries.

The following regulatory systems are particularly relevant.

The European Union's Medical Devices Regulation is particularly relevant when a digital twin performs medical-device functions. The GDPR may also apply to personal and genetic information.

The US FDA framework becomes relevant when the software meets the applicable definition of a medical device.

In India, the Medical Devices Rules 2017 and the Digital Personal Data Protection Act 2023 are important parts of the regulatory analysis. The applicable commencement provisions, exemptions and sector-specific requirements must also be considered.

12. Major Regulatory Challenges

The development of digital twin organisms creates several unresolved legal problems.

1. Regulatory uncertainty

There is no universally recognised classification specifically for digital twin organisms.

2. Biological identity

A digital representation may reproduce biological characteristics without becoming the actual organism.

3. Consent disputes

Patients may not fully understand how their biological information will be used in future simulations.

4. Algorithmic accountability

It may be difficult to identify responsibility when a prediction is generated through complex machine-learning systems.

5. Commercial exploitation

Private companies may commercially exploit digital models developed from patient-derived biological information.

6. Cross-border data transfers

Genetic and medical information may be transferred to overseas cloud-computing providers, creating privacy and jurisdictional conflicts.

7. Scientific reliability

A digital twin may produce convincing but inaccurate predictions when based on incomplete biological information.

8. Regulatory fragmentation

Medical-device, privacy, research, intellectual property and biosafety laws may apply simultaneously.

13. Recommendations for a Digital Twin Organism Regulatory Framework

A comprehensive regulatory framework should introduce the following safeguards:

Risk-based classification according to the twin's intended use.

Mandatory validation for clinically significant applications.

Informed consent for the use of identifiable biological information.

Independent scientific and ethical oversight.

Clear allocation of responsibility between developers and healthcare providers.

Cybersecurity and data-protection standards.

Transparent documentation of model limitations.

Post-market monitoring and incident reporting.

Rules governing commercial use of patient-derived digital models.

International cooperation concerning cross-border biological data.

A separate statutory framework may eventually be justified for high-risk digital biological models, but it should complement rather than unnecessarily duplicate existing medical-device and privacy legislation.

14. Conclusion

Digital Twin Organism Regulatory Status represents an emerging area of biotechnology, medical-device and digital governance law.

The most important legal principle is that a digital twin must be regulated according to its actual functionality, intended purpose and potential impact rather than merely its technological description.

In Australia, the Therapeutic Goods Act 1989, Therapeutic Goods (Medical Devices) Regulations 2002 and Privacy Act 1988 provide important existing regulatory foundations.

The cases of Snitem and Philips France, D'Arcy v Myriad Genetics, Rogers v Whitaker, Wallace v Kam and Moore v Regents offer relevant judicial principles concerning medical software, biotechnology, informed consent, liability and biological materials.

However, none of these decisions directly establishes a legal personality or comprehensive regulatory category for digital twin organisms.

The emerging legal position is therefore that digital twin organisms are generally regulated through existing legal categories, with their precise status depending on their intended use and associated risks.

15. Important Case Law Reference List

16. Important Legislation Reference List

Therapeutic Goods Act 1989 (Cth).

Therapeutic Goods (Medical Devices) Regulations 2002 (Cth).

Privacy Act 1988 (Cth).

Gene Technology Act 2000 (Cth).

Biosecurity Act 2015 (Cth).

Medical Devices Regulation (EU) 2017/745.

General Data Protection Regulation (EU) 2016/679.

Federal Food, Drug, and Cosmetic Act (USA).

Medical Devices Rules 2017 (India).

Digital Personal Data Protection Act 2023 (India).

Useful official legal and regulatory sources:

Australian TGA – Software-based medical device regulation

TGA – AI and medical-device software regulation

Australian Medical Devices Regulations

High Court of Australia – D'Arcy v Myriad Genetics

CJEU – Snitem and Philips France judgment

Research note: This is a comparative academic legal analysis, not a claim that a dedicated Digital Twin Organism Act or DTO-specific judicial precedent already exists.