Arbitrability of Disputes in Predictive-Health Analytics Licensing

Introduction

Predictive-health analytics refers to the use of artificial intelligence (AI), machine learning, big-data analytics, and statistical models to predict disease risks, treatment outcomes, patient deterioration, or healthcare trends. Licensing arrangements in this sector commonly involve software developers, hospitals, pharmaceutical companies, diagnostic laboratories, cloud-service providers, and research institutions.

Disputes arising from predictive-health analytics licensing agreements may concern:

1. Scope of software licence.
2. Royalty and revenue-sharing obligations.
3. Accuracy and performance warranties.
4. Data ownership and usage rights.
5. Confidentiality and trade secrets.
6. Regulatory compliance obligations.
7. Intellectual property ownership.
8. Indemnity for inaccurate predictions or patient harm.
9. Cross-border transfer of health data.
10. Termination and post-termination obligations.

The principal issue is whether such disputes are capable of settlement by arbitration, i.e., whether they are arbitrable.

Under Indian arbitration jurisprudence, disputes involving private contractual rights (rights in personam) are generally arbitrable, whereas disputes involving sovereign functions, public rights, or rights enforceable against the world at large (rights in rem) are ordinarily non-arbitrable. Predictive-health analytics licensing disputes predominantly arise from commercial contracts and are therefore generally arbitrable, subject to certain exceptions.

I. Nature of Predictive-Health Analytics Licensing Disputes

A predictive-health analytics licence typically grants rights relating to:

• Use of AI algorithms.
• Access to predictive models.
• Use of clinical datasets.
• Integration into hospital systems.
• Customization and maintenance.
• Sharing of derived insights.
• Payment of licence fees.
• Compliance with healthcare regulations.

Examples of disputes include:

• Whether the licensee exceeded the authorized use.
• Whether the licensor breached performance guarantees.
• Whether patient datasets were improperly exploited.
• Whether royalties were correctly computed.
• Whether confidential algorithms were misappropriated.

Since these disputes arise from contractual obligations between identified parties, they ordinarily involve rights in personam and are suitable for arbitration.

II. Categories of Arbitrable Disputes

1. Licensing and Royalty Disputes

Disputes regarding:

• Licence fees.
• Revenue-sharing.
• Milestone payments.
• Subscription charges.
• Royalty computation.

These are classic commercial disputes and are fully arbitrable.

Example

A hospital alleges that the AI vendor overcharged subscription fees for predictive oncology software.

Such disputes involve purely contractual rights.

2. Breach of Service-Level Agreements

Predictive-health software licences usually contain Service Level Agreements (SLAs) concerning:

• Uptime.
• Accuracy thresholds.
• Maintenance.
• Technical support.

Failure to comply with such obligations can be resolved through arbitration.

3. Confidentiality and Trade Secret Disputes

AI models often incorporate:

• Proprietary algorithms.
• Source code.
• Training methodologies.
• Clinical insights.

Disputes involving misuse of trade secrets between contracting parties are generally arbitrable because they affect inter se rights only.

4. Data-Usage Disputes

Parties may disagree regarding:

• Secondary use of patient data.
• Commercial exploitation of anonymized datasets.
• Restrictions on model retraining.

Such contractual disputes are ordinarily arbitrable unless they involve statutory violations affecting public rights.

5. Cross-Border Technology Transfer Disputes

Global healthcare technology agreements frequently involve:

• Foreign software licensors.
• Indian hospitals.
• Cloud-hosted predictive platforms.

International commercial arbitration is particularly suitable because it offers:

• Neutral forum.
• Confidentiality.
• Expert adjudicators.
• Enforceability under the New York Convention.

III. Non-Arbitrable Components

Certain disputes connected with predictive-health analytics may be non-arbitrable.

A. Regulatory Enforcement Proceedings

Questions involving:

• Violation of healthcare statutes.
• Government sanctions.
• Regulatory penalties.
• Suspension of medical licences.

cannot ordinarily be determined by arbitrators because they involve public law functions.

B. Criminal Liability

If inaccurate predictive analytics leads to allegations of:

• Fraud.
• Cheating.
• Criminal negligence.
• Data theft.

criminal proceedings remain outside arbitral jurisdiction.

C. Determination of Statutory IP Validity

An arbitral tribunal generally cannot:

• Revoke patents.
• Cancel copyrights.
• Invalidate registered IP rights.

These are rights in rem and require adjudication by statutory authorities or courts. However, contractual questions regarding licensed IP remain arbitrable.

D. Public Health Issues Affecting Society at Large

Where disputes substantially affect:

• Public healthcare access.
• National health programmes.
• Regulatory standards applicable to all citizens,

courts may regard such matters as involving public interest and therefore unsuitable for private adjudication.

IV. Judicial Tests Governing Arbitrability

Indian courts apply several principles.

1. Rights in Personam vs Rights in Rem Test

If the dispute concerns private contractual rights between parties, arbitration is permitted.

If it affects the public generally, arbitration may be barred.

2. Public Policy Test

Disputes involving:

• Sovereign powers.
• Public welfare regulation.
• Criminal sanctions.

are usually non-arbitrable.

3. Exclusive Statutory Forum Test

Where legislation confers exclusive jurisdiction on specialized authorities, arbitration may be excluded.

V. Important Case Laws

1. Booz Allen & Hamilton Inc. v. SBI Home Finance Ltd.

Principle: The Supreme Court distinguished between rights in rem and rights in personam.

Held:
Rights in personam are arbitrable; rights in rem are generally non-arbitrable.

Relevance:
Predictive-health licensing disputes are primarily contractual and therefore usually arbitrable.

2. Vidya Drolia v. Durga Trading Corporation

Principle:
Established a four-fold test for determining arbitrability.

Held:
Disputes affecting sovereign or public interests are non-arbitrable.

Relevance:
Healthcare regulatory disputes affecting public welfare may fall outside arbitration, whereas licensing disputes remain arbitrable.

3. Eros International Media Ltd. v. Telemax Links India Pvt. Ltd.

Held:
Contractual disputes concerning copyright licences are arbitrable.

Relevance:
Predictive-health software licensing disputes involving AI platforms can similarly be arbitrated when they concern contractual obligations rather than statutory rights.

4. A. Ayyasamy v. A. Paramasivam

Held:
Mere allegations of fraud do not automatically make disputes non-arbitrable.

Relevance:
Claims that predictive algorithms were intentionally manipulated may still be arbitrable unless serious criminal wrongdoing is involved.

5. Avitel Post Studioz Ltd. v. HSBC PI Holdings (Mauritius) Ltd.

Held:
Commercial fraud disputes are generally arbitrable unless they affect public rights.

Relevance:
Misrepresentation regarding predictive accuracy or software capabilities may be resolved through arbitration.

6. Indian Performing Right Society Ltd. v. Entertainment Network (India) Ltd.

Held:
Contractual aspects of intellectual property arrangements may be arbitrated.

Relevance:
Disputes concerning licence scope, royalties, or usage restrictions in predictive-health analytics are generally arbitrable.

7. Lifestyle Equities CV v. QDSeatoman Designs Pvt. Ltd.

Held:
Pure infringement actions involving statutory rights may remain non-arbitrable.

Relevance:
If a dispute seeks cancellation or invalidation of AI-related IP rights, arbitration may not be available.

VI. Challenges in Arbitrating Predictive-Health Analytics Disputes

1. Complex technical evidence.
2. Need for medical and AI experts.
3. Data privacy concerns.
4. Cross-border regulatory compliance.
5. Multi-party proceedings involving hospitals, patients, and insurers.
6. Potential overlap between private disputes and public health regulation.

Despite these challenges, arbitration remains attractive because it ensures confidentiality, flexibility, expert adjudication, and international enforceability.

Conclusion

Disputes in predictive-health analytics licensing are predominantly arbitrable because they arise from private commercial agreements involving software licences, data-sharing arrangements, royalties, confidentiality obligations, and performance warranties. However, matters involving statutory IP validity, criminal misconduct, healthcare regulation, or broader public health concerns remain non-arbitrable. Indian jurisprudence strongly supports arbitration of contractual technology and licensing disputes, provided that the dispute concerns rights in personam rather than rights in rem.