Nanobots in Healthcare: A Systematic Review of Emerging Clinical Applications and Future Directions

 Nanobots in Healthcare: A Systematic Review of Emerging Clinical Applications and Future Directions

Abstract

Nanobots—engineered micro- and nanoscale robotic devices capable of performing tasks at the cellular and molecular level—represent an emerging paradigm in precision medicine. Recent advances in nanofabrication, biohybrid integration, artificial intelligence (AI), and wireless actuation have accelerated the progress of nanobots from theoretical constructs toward practical biomedical applications. These devices are now being engineered for targeted drug delivery, minimally invasive surgery, in vivo diagnostics, real-time biosensing, and the navigation of complex physiological environments. Despite substantial promise, the translation of nanobots into clinical medicine faces considerable challenges, including biocompatibility, immune clearance, real-time control, manufacturing scalability, regulatory uncertainty, and ethical considerations.

Keywords

Nanobots; nanorobotics; nanomedicine; targeted drug delivery; biohybrid nanorobots; artificial intelligence; precision medicine; nano-theranostics; in vivo diagnostics; micro-swimmers.

1. Introduction

Nanobots are nanoscale robotic systems capable of interacting with biological environments to diagnose, treat, or prevent disease at the molecular level. Unlike conventional therapies that distribute drugs throughout the body, nanobots enable spatially and temporally precise medical intervention. Recent advancements in nanomanufacturing, smart materials, and machine learning have established feasibility for clinical translation.

Systematic Review Methods

A scoping systematic review methodology was employed to capture the breadth of emerging nanobot technologies in medical applications. Literature from 2020–2025 was screened across databases including PubMed, Scopus, Web of Science, and IEEE Xplore. Search terms included: nanobot, nanorobot, nanomedicine, microswimmer, biohybrid robot, targeted drug delivery, and precision medicine.

Inclusion Criteria

• Peer-reviewed research articles focused on in vivo or clinically relevant nanobot applications
• Engineering advancements with direct biomedical implications
• Ethical, regulatory, or translational analyses

Exclusion Criteria

• Purely theoretical nanorobotics with no biological interface
• Macro- or milli-scale robotic interventions
• Non-medical nanofabrication studies

Study quality was assessed based on translational relevance, validated biocompatibility, and clinical maturity indicators such as Technology Readiness Levels (TRLs) and clinical trial progression.

2. Engineering Foundations of Medical Nanobots Foundations of Medical Nanobots

Nanobot engineering integrates nanoscale fabrication, biointerface design, and responsive actuation systems to support functionality within the human body. These devices typically range from 100 nm to several micrometres in diameter, permitting cellular-level interactions while maintaining controlled behaviour in vivo (Sun & Yan, 2020; Wang & Gao, 2022).

2.1 Materials and Biocompatibility

Materials selection determines safety, degradation, and immunogenicity. Common classes include:

• Biodegradable polymers: e.g., PLGA for enzymatic breakdown
• Metallic nanostructures: e.g., magnetic iron oxide for navigation
• Carbon-based materials: graphene derivatives for functional surfaces
• DNA-origami architectures: precise binding and molecular logic

Strategies for immune evasion involve PEGylation and biomimetic coatings such as cell membranes to reduce macrophage recognition (Subramanian & Lee, 2023).

2.2 Propulsion and Navigation Mechanisms

Propulsion methods must overcome physiological barriers such as viscous drag at low Reynolds numbers. Current actuation modalities include:

• Magnetic actuation — precise external field control (Feng & Zhao, 2024)
• Ultrasound propulsion — vascular manoeuvrability (Li et al., 2022)
• Chemical propulsion — catalytic surface reactions
• Biohybrid motility — leveraging flagellar forces or muscle microtissues

2.3 Wireless Control and External Actuation

Electromagnetic, acoustic, and optical signals are used to coordinate swarms, enabling navigation through complex biological geometries (Ayyagari et al., 2024). AI-enhanced predictive control improves trajectory optimisation and treatment response.

 

3. Biohybrid and AI-Enabled Nanorobotics

The convergence of biology and robotics has driven the creation of biohybrid nanoswimmers, often composed of living cells or biological components that confer self-motility and sensory feedback (Belling et al., 2021).

3.1 Biohybrid Micro-Swimmers

Examples include:

• Bacteria-driven nanobots targeting hypoxic tumours
• Sperm-hybrid microrobots for reproductive medicine
• Red blood cell–camouflaged devices for vascular delivery

These systems demonstrate inherent biocompatibility and autonomous environmental responsiveness.

3.2 Autonomous Decision-Making and Logic Systems

AI integration enables robotic nanodevices to:

• Detect disease signatures
• Adapt tasks based on real-time biomarker feedback
• Optimise drug release kinetics

DNA and protein-based logic gates support in vivo computation for selective therapy activation (Jiang et al., 2023).

4. Clinical Application Domains

Nanobots are being evaluated for disruptive applications across major clinical fields, with oncology leading translational progress (Amin et al., 2023; Attia et al., 2020).

4.1 Oncology: Precision Tumour Targeting

Nanobots offer tumour-specific delivery with reduced systemic toxicity by exploiting:

• Hypoxia-targeting ligands for deep tumour penetration
• pH-responsive logic gates for controlled drug release
• Real-time biomarker detection for adaptive therapy

Clinical studies report enhanced intratumoral accumulation and reduced chemotherapeutic dosages (Qadir et al., 2024).

 

 

4.2 Neurology and Blood–Brain Barrier Bypass

Crossing the blood–brain barrier (BBB) remains a critical challenge in treating neurodegenerative diseases. Magnetically guided nanobots have been shown to traverse BBB tight junctions without structural compromise (Kumar & Prasad, 2024), offering therapeutic access to conditions such as:

• Parkinson’s disease
• Glioblastoma
• Alzheimer’s disease

4.3 Cardiovascular Intervention

Nanomotors capable of circulating in the bloodstream enable site-specific treatment of:

• Atherosclerotic plaque
• Thrombus dissolution (Feng & Zhao, 2024)

Magneto-acoustic navigation supports real-time thrombolysis without invasive surgery.

4.4 Diagnostics and In-Body Biosensing

Nanoscale biosensors integrated into robotic systems enable:

• Continuous disease monitoring
• Early infection detection
• Real-time physiological telemetry (Tiwari et al., 2022)

Biosensing nanobots may operate as distributed networks communicating with AI predictive systems.

Table 1 — Clinical Application Domains vs. Technology Readiness & Clinical Phase

Clinical Domain	Technology Status (summary)	TRL (estimate)	Clinical Phase (evidence)	Representative studies
Oncology	Advanced preclinical and several early in vivo efficacy demonstrations; magnetic guidance shows promise for targeted tumour delivery.	5–6	Preclinical → Early Phase I (select trials/large-animal work)	Zhang et al., 2024; Martel, 2020; Naikwadi et al., 2024; Fu et al., 2025.
Neurology (BBB)	Promising biohybrid strategies and magnetically assisted delivery; limited early in vivo CNS studies	4–5	Preclinical (rodent) → Early feasibility	Li et al., 2023; Xu, 2024; Kumar & Prasad, 2024.
Cardiovascular (thrombolysis, plaque)	Magneto-acoustic approaches demonstrate thrombus reduction in animal models	4–5	Preclinical (large-animal models emerging)	Feng & Zhao, 2024; Park et al., 2024.
Diagnostics & Biosensing	Rapidly maturing; nanosensors integrated with nanorobots for in vivo detection in animal studies	5	Preclinical → Early clinical imaging agents	Tiwari et al., 2022; Sun & Yan, 2024.
Infectious disease & Antimicrobials	Early exploratory studies for targeted antimicrobial delivery; proof-of-concept in vitro/in vivo	3–4	Preclinical	Velluvakandy et al., 2025; Weerarathna, 2025.
Regenerative medicine & Tissue engineering	Conceptual biohybrid strategies and scaffold-guided nanorobots under development	3–4	Preclinical (in vitro / small-animal)	Zarepour et al., 2024; Belling et al., 2021.

Notes: TRL estimates are based on literature indicators of device demonstration, in vivo efficacy, and reproducible control methods. Clinical phase follows standard therapeutic trial progression where applicable. The TRL and clinical-phase mapping is intentionally conservative to reflect translational uncertainty.

 

Graphs and Quantitative Visuals (Descriptions & Data Sources)

Graph 1 — Research Distribution by Clinical Field (2020–2025)

Description: A bar chart representing the relative frequency of peer-reviewed publications focused on nanobots in Oncology, Neurology, Cardiovascular, Diagnostics, Infectious Diseases, and Regenerative Medicine during 2020–2025. Data sources for counts include PubMed, Scopus and Web of Science searches using systematic search strings (see Methods). Representative systematic reviews and PMC articles confirm increasing publication activity, especially in oncology and diagnostics (Sun et al., 2024; Zhou, 2021; Fu, 2025).

 

Bar chart with oncology highest, diagnostics second, neurology and cardiovascular moderate, infectious diseases and regenerative medicine lower.

Graph 2 — Projected Clinical Maturity Timeline by Domain

Description: A conceptual timeline chart (Gantt-style) showing conservative projected maturation windows for each clinical domain from Preclinical → Early Clinical → Widespread Clinical Adoption. Projections are informed by TRL estimates and recent translational milestones (Martel's MRI-guided navigation work; rising number of in vivo efficacy studies). This is a conceptual, evidence-informed projection rather than a statistical forecast.

 

Timeline with oncology entering early clinical phases sooner than neurology; diagnostics show earlier translational potential.

 

System Architecture of AI-Enabled Nanobot Systems (Hybrid Style)

System diagram illustrating components of an integrated AI-enabled nanobot platform: (1) Design & fabrication pipeline (materials & functional payloads), (2) External actuation & imaging (MRI/ultrasound/optical), (3) On-board sensing & molecular logic, (4) Edge/Cloud AI for navigation and decision support, (5) Clinical operator interface and safety interlocks. Arrows indicate data flows and feedback loops.

  

 

Flow diagram with five modules connected by bidirectional arrows showing sensor data feeding into AI control, clinician oversight, and actuation systems.

 

5. Regulatory, Manufacturing, and Safety Challenges

Translation into clinical environments requires navigating multiple barriers:

• Toxicology and immunogenicity: Understanding long-term clearance is urgently needed (Gigli et al., 2020).
• Standardisation: Lack of consensus in nanorobot characterisation complicates approval (Rana et al., 2024).
• Ethical governance: Complex socio-technical risks require proactive frameworks (Gupta & Sharma, 2025).

Scalable production that maintains nanoscale precision remains an unresolved engineering hurdle.

6. Ethical and Socio‑Economic Considerations

Nanorobotics introduces novel ethical dilemmas and responsibility challenges. Key concerns include:

• Autonomy and consent: Continuous in‑body operation may enable intervention without patient awareness.
• Accountability: Assigning liability when autonomous systems malfunction remains legally ambiguous (Singh et al., 2025).
• Equitable access: Without governance safeguards, nanorobot therapies risk deepening global health inequalities.
• Privacy and biosurveillance: Diagnostic nanobots capable of real-time monitoring could be misused beyond clinical intent.

Regulatory frameworks must evolve to ensure safety, transparency and fair distribution (Gupta & Sharma, 2025).

7. Future Directions in Precision Micro‑Medicine

Key technological frontiers expected to accelerate clinical realisation include:

• Adaptive swarms capable of cooperative task allocation
• End‑to‑end closed‑loop therapy integrating AI diagnostics and nanobot execution
• Patient‑specific actuation maps using digital twins and personalised biophysical modelling
• Living nanorobots employing genetically engineered cells for bio‑integration (Belling et al., 2021)
• Real‑time biodegradation control to ensure safe clearance post‑mission

Regulatory sandboxes and early‑access clinical pathways will be pivotal for responsible deployment.

8. Conclusion

Nanobots represent the emergence of precision micro-medicine: a shift from systemic treatment to molecular-level intervention. While advances in materials science, AI-enhanced navigation, and biohybrid engineering have driven breakthroughs in oncology, neurology, cardiovascular medicine and diagnostics, extensive research is required to ensure safe, ethical and equitable translation. The coming decade will likely determine whether nanobots remain a promising vision or become a clinical revolution.

9. References

The reference list below consolidates the article's cited literature and additional recommended readings. Entries are formatted in APA 7th style and include 2020–2025 publications prioritised in the systematic search.

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