Author: Vijay R. Agale, Balaji Law College affiliated with Savitribai Phule Pune University, Pune
To the Point
The rapid rise of Artificial Intelligence (AI) in the healthcare sector has opened new frontiers in diagnosis, treatment, and patient care. Yet, in 2025, as hospitals and clinics increasingly adopt AI tools for everything from radiology analysis to drug prescriptions, a pressing legal debate takes shape. How do we safeguard patient autonomy when machines are making clinical decisions? Who is accountable when algorithms error? And are our current laws equipped to handle the rights and protections patients deserve in this era of digital healthcare?
This article unpacks these layered questions, critically examining the intersection of AI innovation with healthcare jurisprudence. It explores how autonomy, accountability, and data privacy are being reshaped by the presence of intelligent machines in the medical landscape—and what this means for patient rights in today’s tech-infused clinical world.
Legal Analysis
As AI becomes an integral part of clinical decision-making, the legal system is under increasing pressure to reinterpret existing norms and introduce new frameworks. Central to this conversation is the principle of patient autonomy, a foundational pillar of medical law and ethics, which may be compromised when AI systems recommend or even autonomously carry out medical interventions.
One of the biggest conundrums is the doctrine of informed consent. Traditionally rooted in the physician-patient relationship, consent requires full disclosure of risks, benefits, and alternatives by a medical professional. But when AI steps in as the ‘co-pilot’ or even ‘pilot’ of care decisions, how much must be disclosed about the algorithm’s functioning? And can patients truly give informed consent if they don’t understand how AI reaches its conclusions?
Equally complex is the attribution of liability in the event of AI-related errors. If a misdiagnosis results from algorithmic oversight, does the blame lie with the developer, the healthcare provider using it, or the AI system itself—particularly if we move toward the controversial notion of legal personhood for AI?
Then comes the matter of algorithmic bias. AI tools trained on incomplete or skewed datasets can perpetuate or amplify systemic inequalities in healthcare delivery. Such biases might violate constitutional guarantees of equality and non-discrimination and invoke legal scrutiny under equal protection clauses.
On the privacy front, AI systems rely on enormous troves of sensitive health data, raising urgent concerns under data protection legislation. The legal burden shifts toward ensuring robust governance mechanisms, data anonymization, and cybersecurity standards, all while respecting a patient’s right to data ownership and control.
Lastly, the black box problem—wherein AI decisions are opaque even to developers—demands legal intervention for transparency and explainability. Without these, accountability becomes nearly impossible.
The Proof
The proof of the challenges posed by AI in healthcare is no longer theoretical—it’s playing out in real time across hospitals, clinics, and courtrooms worldwide.
Take, for example, AI-powered imaging tools now common in radiology. These tools can identify tumors, fractures, or hemorrhages with astonishing accuracy. However, when an AI tool misses a diagnosis, the question arises: was the radiologist negligent for relying on the tool? Or was the fault embedded in the algorithm’s training data or its design?
This directly impacts informed consent. In a world where patients are often unaware that an AI tool contributed to their diagnosis or treatment plan, the process of consent is incomplete. The law now needs to define what constitutes “adequate disclosure” when AI is involved.
Similarly, medical malpractice law is being stress-tested. Traditionally, malpractice hinges on the “reasonable physician” standard. But what happens when the “decision” came from a machine, not a person? Courts will soon need to determine whether design flaws, programming errors, or even lack of oversight over an AI tool can establish liability—and if so, for whom.
Then there’s the issue of algorithmic discrimination. Imagine an AI system that has been trained primarily on data from urban, upper-income patients. When applied to rural or underrepresented populations, it may produce less accurate diagnoses or inappropriate treatment recommendations, unintentionally reinforcing disparities. Proving such discriminatory impact in court will require a new kind of evidence—data audits, bias detection frameworks, and perhaps even algorithmic impact assessments.
The data privacy puzzle grows ever more intricate. In 2025, AI systems analyze millions of patient records, often aggregating data across hospitals, labs, and even wearable devices. These systems must comply with India’s Digital Personal Data Protection Act, 2023, among other evolving regulations. Healthcare providers must now ensure that patients’ data is collected, stored, and processed with due diligence, and breaches—whether by hackers or system failures—could invite both civil and criminal liability.
Finally, the lack of transparency in AI decision-making creates both ethical and legal blind spots. If neither patient nor physician understands how a decision was reached, it erodes trust. It also complicates legal claims for negligence or misdiagnosis, as plaintiffs struggle to demonstrate how and why the harm occurred.
Abstract
This article provides a critical exploration of the jurisprudence evolving around Artificial Intelligence in healthcare in 2025. It investigates the challenges posed by AI’s growing autonomy in clinical settings, particularly regarding patient autonomy, informed consent, and the attribution of liability in medical errors. It also discusses algorithmic bias, data privacy, and transparency—issues that intersect core constitutional rights and healthcare ethics. Drawing upon contemporary legal principles and anticipating future case law, the article makes a case for reimagining healthcare law to meet the demands of an AI-driven clinical world, ensuring patient rights are not sidelined in the rush toward innovation.
Legal statutory Interpretation
While India has not yet witnessed Supreme Court rulings explicitly dealing with AI in healthcare, several legal trajectories are emerging, and future decisions will likely borrow from analogous legal domains.
Informed Consent and AI: Courts may build on precedents concerning informed consent in complex medical procedures. Any future litigation will likely ask whether patients were adequately informed about the AI’s role, its limitations, and the risks associated with its use.
Medical Negligence & AI Tools: Consider how courts have handled cases involving defective medical devices or negligent use of software in diagnostics. These rulings might inform how judges evaluate AI-assisted errors—especially in establishing the chain of causation.
Bias and Discrimination: Future claims may invoke Article 14 of the Constitution (Right to Equality) if AI tools are proven to produce racially or socioeconomically biased outcomes. Courts may demand demonstrable links between biased training data and the resulting harm.
Data Privacy Violations: Legal battles may arise under the Digital Personal Data Protection Act or IT Act, especially around breaches of sensitive health information or unauthorized AI usage. Here, patient consent, data minimization, and anonymization will take center stage.
Demand for Algorithmic Transparency: Legal challenges demanding explainability may draw parallels with Right to Information jurisprudence. Patients may argue that opaque algorithms violate their right to make informed healthcare decisions or to seek redress for malpractice.
Regulatory Gaps: Legal challenges might target the absence or inadequacy of regulations governing AI in medicine. Plaintiffs may push for judicial activism or Public Interest Litigations (PILs) urging the state to establish robust oversight mechanisms under Article 21—the right to life and health.
Conclusion
Artificial Intelligence in healthcare is not just a technological revolution—it is a legal and ethical reckoning. In 2025, as AI tools become smarter and more autonomous, they challenge the very scaffolding of medical law that has served patients for decades.
Patient autonomy, once safeguarded through human interaction and informed dialogue, now risks being undermined by opaque, data-driven systems. Traditional models of liability and malpractice are no longer sufficient to cover the AI-human hybrid landscape of clinical care. The potential for algorithmic bias adds a troubling dimension to health equity, and the need to protect sensitive patient data has never been more critical.
To navigate these challenges, India needs a proactive legal roadmap—one that balances innovation with patient protection, and efficiency with equity. This means revisiting consent laws, redefining malpractice standards, mandating algorithmic transparency, and strengthening data protection regulations. It also means fostering collaboration among legal experts, healthcare professionals, and AI developers to ensure that technology serves, rather than supplants, the patient.
The AI revolution in medicine is here. It’s time our laws caught up.
FAQS
How does AI challenge the traditional concept of patient autonomy?
AI may recommend or even implement treatment decisions autonomously, reducing the direct role of physicians and potentially limiting patient understanding and choice.
Who could be held liable for a medical error caused by AI?
Depending on the context, liability could fall on the developer of the AI, the healthcare provider using it, or—speculatively in the future—the AI system itself.
How are data privacy laws affected by the use of AI in healthcare?
AI’s dependence on large-scale patient data requires strict adherence to data protection laws, with emphasis on consent, data security, and patient control.
Why is transparency in AI algorithms important in healthcare law?
Transparency ensures patients and physicians can understand and trust AI-driven recommendations, and it is vital for assigning accountability in case of errors.
Are there specific regulations for the use of AI in healthcare in India in 2025?
Not yet comprehensive, but AI usage is currently governed by a combination of medical practice laws, data protection regulations, and ethical guidelines, with more specific frameworks expected.
How might the concept of medical malpractice evolve with the increasing use of AI?
It may need to include failures in AI design, deployment, or oversight, moving beyond human negligence alone.
Could AI systems be granted legal personhood in the future, and what would be the implications?
While highly speculative, if AI were granted legal personhood, it could bear responsibility and rights—but this would significantly alter existing liability structures.
References
Constitution of India
Digital Personal Data Protection Act, 2023
IT Act, 2000
