Revolutionizing Laboratory Practices: Pioneering Trends in Total Laboratory Automation

Abstract

Total laboratory automation (TLA) is a transformative solution in clinical laboratories that addresses growing demands for operational efficiency, accuracy, and rapid turnaround times in patient care. TLA integrates advanced technologies across pre-analytical, analytical, and post-analytical phases, thereby streamlining workflows, reducing manual intervention, and enhancing QC. TLA adoption is driven by factors such as increasing test volumes, the need for cost reduction and regulatory compliance, and labor shortages. Key benefits of TLA include improved accuracy through error minimization, optimized resource utilization, enhanced staff well-being, and consistent delivery of high-quality results. Leading companies, including Abbott, Roche, Siemens, and Beckman Coulter, dominate the global TLA market with innovative solutions. Recent developments incorporate artificial intelligence (AI), machine learning, robotics, and Internet-of-things technologies, which enable predictive analytics and automated data management. However, challenges remain, including high implementation costs, the need for workforce training, cybersecurity concerns, and system integration complexities. Future trends indicate that TLA will advance through enhanced AI integration, sustainable practices, and big data analytics, fostering continuous improvements in precision diagnostics and clinical outcomes. Moreover, TLA has the potential to revolutionize laboratory operations globally, driving efficiency, accuracy, and sustainability while ultimately improving patient care. Successful adoption of TLA will require strategic planning, interdisciplinary collaboration, and alignment with emerging healthcare needs. In this review, we emphasize that overcoming these challenges through innovation and robust management is essential for ensuring that TLA continues to play a vital role in modern healthcare systems.

INTRODUCTION

In modern hospitals, laboratory test results are critical in patient care, particularly in acute medical settings, where predictable and short turnaround times (TATs) are essential. To meet these demands, core laboratories should adopt advanced technological innovations in information technology, including analytical platforms, track systems, and middleware, which are facilitated by total laboratory automation (TLA) [1]. Additionally, specialized laboratory sections must collaborate closely with multidisciplinary medical teams to effectively address specific clinical conditions.

The emergence of TLA in clinical laboratories is driven by several key factors that collectively require automation, including the need to manage increasing test volumes, enhance efficiency and accuracy, ensure compliance with regulatory requirements, reduce operational costs, remain competitive in a rapidly evolving healthcare landscape, and address labor shortages. Technological advancements have enabled the development of sophisticated automation systems that fundamentally transform laboratory operations to meet the growing demands of the healthcare industry.

The primary objective of clinical laboratories is to ensure high-quality and on-time service delivery, with high-quality test results [2, 3]. TLA has become a pivotal solution for achieving these objectives by standardizing processes, improving QC, and minimizing manual interventions across the pre-analytical, analytical, and post-analytical phases of laboratory testing. This integration significantly enhances laboratory performance and operational efficiency. When implemented effectively, TLA systems can lower laboratory costs, improve patient outcomes, and address key challenges such as staff job satisfaction, the length of patient stay, safety, and financial sustainability [4].

TLA systems are widely used in clinical chemistry, diagnostic immunology, and hematology laboratories, with increasing applications in clinical microbiology [5, 6]. Notably, considerable advancements in laboratory automation have been recently achieved in clinical microbiology [6–10]. The ongoing development of TLA within these and other specialized fields will continue to drive the transformation of laboratory services, ensuring alignment with the complex demands of modern healthcare.

OVERVIEW OF TLA

Definition and components of TLA

TLA refers to the end-to-end automation of pre-analytical, analytical, and post-analytical processes within a clinical laboratory. The pre-analytical phase encompasses automated systems for sample collection and transportation, such as pneumatic tube systems, conveyor tracks, and sample transport robots, along with sample reception (using barcode scanning for identification), sorting, and preparation (including automated aliquoting and centrifugation). The analytical phase involves automated analyzers integrated via middleware systems that enable seamless coordination among different platforms. In the post-analytical phase, automated validation of test results is performed according to predefined criteria, followed by integration with electronic health record (EHR) systems [11]. Sample archiving and cooling systems are crucial in maintaining specimen integrity, enabling rapid retesting, supporting regulatory compliance, and facilitating efficient sample management [12–14]. The three phases are interconnected via a combination of automation tracks, robotics, computer systems, and laboratory information systems (LIS), ensuring smooth and efficient workflows across all stages of the testing process.

Expected benefits of TLA implementation in clinical laboratories

Increased efficiency

TLA enhances the efficiency of laboratory workflows by automating each step, from pre-analytical to post-analytical stages. This integration leads to faster processing times, higher throughput, and the ability to manage larger sample volumes using the same or even fewer resources [15]. Incorporating a sound alarm, color-coded lamps, or other notification system into TLA systems to promptly alert operators to errors or critical situations would be highly beneficial, ensuring timely responses and minimizing potential disruptions (Fig. 1).

Fig. 1. Expected benefits of total laboratory automation implementation in clinical laboratories.

Abbreviation: TAT, turnaround time.

Enhanced accuracy and consistency

Automation minimizes human error, ensuring higher accuracy and consistency in test results. Automated systems maintain precision across each step of the testing process, leading to more reliable outcomes [16]. Notably, errors tend to occur more frequently in the pre- and post-analytical phases than in the analytical phase [17, 18]. Recent proposals for quality indicators (QIs) emphasize monitoring these peri-analytical phases, and TLA systems are expected to improve such quality metrics [19].

Improved working environment

By automating repetitive and labor-intensive tasks, TLA reduces the physical and mental strain on laboratory personnel. This allows staff to focus on more complex and intellectually engaging activities, such as data analysis and interpretation, which can lead to greater job satisfaction and reduced burnout rates.

Cost reduction

Although the initial investment in TLA systems can be significant, the long-term savings are substantial. Automation reduces labor costs by minimizing the need for manual intervention and optimizes operational efficiency by enabling the more effective use of reagents and consumables [20, 21]. Additionally, TLA lowers the likelihood of costly errors and reduces the need for overtime.

Faster TATs

Automated systems expedite sample processing and analysis relative to that of manual methods, quickening the delivery of test results. This rapid turnaround is critical for timely diagnosis and treatment, particularly in emergency situations [4, 22–25].

Data management and traceability

TLA systems ensure accurate data management and traceability through advanced software, seamless integration with LIS and EHR systems, and middleware for automated QC and process oversight, supporting regulatory compliance and operational efficiency [26].

Optimized space utilization

Space is required for installing automation systems, and modern automation systems are designed with space efficiency in mind, enabling laboratories to maximize the use of available areas. In space-constrained environments, laboratories can enhance their processing capacity without substantial physical expansion by adopting modular analyzers or installing automated tracks on the floor or along the ceiling.

Improved customer satisfaction

The combination of shorter TATs, improved accuracy, and consistent test results enhances patient and healthcare provider satisfaction [27]. Timely and reliable results contribute to better patient care and foster trust in the laboratory’s services [28]. Some TLA systems are equipped with the ability to properly divide and store residual samples after testing for reexamination or research.

PROS AND CONS OF EACH SYSTEM

Global players in TLA and market share status

Although discrepancies exist in the reported market size and share for different resources, the global leading companies in the medical TLA market include Abbott Laboratories (representative product: GLP systems Track), Beckman Coulter (DxA 5000 and Power Express), Roche (cobas connection modules: CCM), Siemens Healthineers (Aptio automation), and Thermo Fisher Scientific (TCAutomation). In 2022, these top players collectively accounted for approximately 93% of the global laboratory automation market revenue [29] (Fig. 2A). Other notable players in the TLA market include BD (BD Kiestra), IDS (IDS-CLAS X-1 Series), and A&T (CLINILOG V4). The current global market size of TLA systems was estimated at 5.57–6.1 billion USD in 2023, and the regional market share is the largest in North America (38%). The Asia–Pacific region is predicted to grow at the fastest pace in the global market (Fig. 2B) [30]. In Korea, 115 institutions operated TLA systems in 2024, including tertiary general hospitals (36 out of 47, 77%), general hospitals (58 out of 331, 18%), and large commercial laboratories and health check-up centers (21 out of 52, 40%). The current market share percentages of each system in Korea are as follows: Roche (39%), Beckman Coulter (14%), Siemens (13%), Hitachi (13%), A&T (12%), and Abbott (7%).

Fig. 2. Global market shares by key players (A) and regions (B) in total laboratory automation systems in clinical laboratories.

Technical characteristics of major TLA systems

Table 1 provides a comprehensive overview of TLA systems from five leading manufacturers in the clinical laboratory industry. Each system offers specific characteristics across the pre-analytical, analytical, and post-analytical phases of laboratory testing. While all systems offer automation throughout the laboratory workflow, the key differentiators lie in their specific technologies for sample transportation, their capacity for high-volume input, the flexibility of their routing systems, and their ability to integrate with various analyzers. An official classification of clinical TLA systems is not yet available, but they can be classified according to the connectivity and characteristics of the sample carrier. In terms of connectivity, TLA systems can be divided into open and closed systems. Open systems, such as Abbott GLP systems Track and Siemens Aptio automation, can connect various heterogeneous instruments produced by numerous companies. Closed systems, such as Roche CCM, can only be connected to specific devices, generally the companies’ instruments. In terms of the sample carrier form, most current systems use single carriers or conveyor belts. Single carrier systems offer precise sample tracking but may have limited throughput. Conveyor belt systems provide high-speed sample transport but can be less flexible for complex routing. The Abbott iCAR system combines the advantages of both, offering independent high-throughput sample routing. However, it may require more complex infrastructure, and Roche CCM transfers samples in racks, not in single carriers (Table 2).

Table 1. Characteristics of major TLA systems in the pre-analytical, analytical, and post-analytical stages.

Manufacturer (TLA model)	Pre-analytical	Analytical	Post-analytical
Abbott Diagnostics (GLP system)	High-volume sample loading via input/output or bulk loader module; automated barcode identification, centrifugation, decapping, and aliquoting	iCAR technology for flexible sample transport; real-time monitoring capacity; dynamic sample management	Automated recapping, temperature-controlled storage, disposal
Beckman Coulter (Power Express)	Automated sample reception, sorting, barcode reading, centrifugation, and decapping	Sample transportation via track system; automated testing with real-time monitoring; intelligent sample routing	Automated recapping, storage, disposal; comprehensive result reporting
Roche Diagnostics (CCM)	Automated sample reception, sorting, barcode reading, centrifugation, decapping, and aliquoting	Integrated testing capabilities with real-time monitoring; efficient sample transportation	Automated recapping, storage, and disposal; robust result reporting system
Siemens Healthineers (Aptio)	Comprehensive sample processing with efficient handling of pre-analytical tasks	Streamlined testing with intelligent routing; advanced real-time monitoring capabilities	Efficient result management, storage, and disposal systems
Thermo Fisher (TCAutomation)	High-volume sample loading; automated centrifugation, decapping, and aliquoting with customizable labeling	Flexible analyzer interfaces supporting various manufacturers; automated sample transportation with real-time monitoring	Automated recapping, sorting, refrigerated storage, and retrieval; comprehensive result reporting

Abbreviations: TLA, total laboratory automation; CCM, cobas connection modules.

Table 2. Advantages and limitations of major TLA systems from different manufacturers.

Manufacturer	Name	Advantages	Limitations	Connectivity	Carrier	Moving	Vertical track	Maximum track throughput (tubes/hr)	Input throughput (tubes/hr)	Centrifuge capacity
Abbott	GLP systems Track	iCAR: independent sample movement system with a 3-dimensional track; large sample refrigerator (21,000 tubes/module); no air compressor; NFC-tagged sample carrier	No sample status check	Open	Single carrier (iCAR)	Self-driving technology	Yes (spiral/bridge)	8,000	900/input module	360/module
Beckman Coulter	Power express	Various third-party connections; the world’s second-most installed, RFID tube routing	Small sample refrigerator (5,000 tubes/module); other rack types unavailable; no automatic disposal function in refrigerator	Open	Single carrier	Conveyor belt	No	1,200	1,200	300
Siemens	Aptio	Various third-party connections; large sample refrigerator (15,360 tubes/module); world’s most installed; RFID carrier recognition	No sample status check; other rack types unavailable; air compressor sound	Open	Single carrier (Puck)	Conveyor belt	No	3,600	1,000	300/module
Roche	CCM	HIL check by camera; 3D track by CCM vertical; large sample refrigerator (13,500 or 27,000 tubes/module); simple structure and sample movement	Closed system; rack should be used for transfer–no free routing; only two preprocessing modules can be connected; air compressor sound; no RFID, only high-resolution camera barcode reader	Closed	Rack	Conveyor belt	Yes (CCM vertical)	1,400/lane	830	495/unit
Thermo Fisher	TCAutomation	Various third-party connections; buffer module; RFID-embedded carrier; large sample refrigerator (14,800 tubes/module); RFID carrier recognition	Other rack types are unavailable; no sample status check	Open	Rack	Conveyor belt	No	1,200	1,200	96/batch

Abbreviations: TLA, total laboratory automation; NFC, near-field communication; RFID, radio frequency identification; HIL, hemolysis; CCM, cobas connection modules.

RECENT TECHNOLOGY TRENDS

Development of innovative automation technology

Modern technological advances have revolutionized medical laboratories and have added substantial value to healthcare and clinical practices [31]. Historically, many key developments in laboratory automation technology have been achieved, such as tube handling, sample preparation, and sample storage, particularly in areas such as clinical chemistry, immunoassays, hematology, and microbiology [32]. The recent integration of technologies such as genomics, mass spectrometry, and microfluidics has advanced various types of omics research, contributing to improved practices for precision medicine [31]. Continuous improvement initiatives, such as implementing systems with single-sample touch capabilities and informatics connectivity, have led to significant enhancements in TAT metrics and overall laboratory efficiency [33]. More innovations are emerging, such as artificial intelligence (AI), digital pathology, liquid biopsies, and 3D printing, that will reshape diagnostics, enhance accuracy, and revolutionize personalized medicine in the future [34].

TLA integration of AI and machine learning (ML)

Intelligent technologies such as AI and ML have positively changed laboratory processes, enhancing sample collection, transmission, and analysis, ultimately improving the accuracy and consistency of medical diagnoses [35]. AI technologies such as deep learning models can optimize laboratory test selection, reducing under- and overutilization problems while improving workflow efficiency [36, 37]. Integrating AI and machine workforce can reduce repetitive manual tasks and optimize workflow management [38–41]. This capability was vital during the coronavirus disease 2019 global pandemic when many laboratories experienced shortages in vital supplies, changes in standard operating protocols, and stay-at-home guidelines; in response, opportunities to leverage the Internet of Things (IoT), connectivity, and AI technology increased [39].

Evolution of data management and analysis technology

The evolution of data management and analysis technology in TLA is rapidly progressing with the integration of advanced solutions such as AI, ML, and high-fidelity computer-aided experimentation [42, 43]. Laboratories are transitioning from manual processes to automated systems, such as laboratory information management systems, which streamline workflow, inspection processes, and sample tracking, enhancing operational speed and accuracy [44]. Machine vision is a term applied to a broad range of industrial-based systems that can replace human inspection, greatly accelerating the production of numerous products and improving quality [45].

TLA APPLICATION EXAMPLES IN VARIOUS FIELDS

Application in the clinical diagnosis field

In clinical diagnostics, TLA has significantly enhanced operational efficiency and clinical outcomes. Automation of tube handling, sample preparation, and storage eliminates repetitive manual tasks, thereby enhancing productivity. TLA can increase test productivity per capita by up to 42% and ensure consistent TATs, with 95% of TLA-based tests reported in less than 120 mins [46]. A new and robust analytical methodology applied in TLA systems for clinical chemistry is liquid chromatography-tandem mass spectrometry (LC-MS/MS) [26].

Beyond clinical chemistry, TLA demonstrates potential across various diagnostic specialties. In microbiology, TLA revolutionizes workflows by automating processes such as inoculation, incubation, and digital imaging, enabling accurate pathogen identification and automated antimicrobial susceptibility testing [5–8]. This advancement accelerates the detection of multidrug-resistant organisms and ensures timely clinical interventions [47]. In hematology, automated systems support blood cell analysis, sorting, and slide preparation, reducing processing steps by over half and significantly decreasing hands-on time [48, 49]. Molecular diagnostics benefit from TLA by integrating automated nucleic acid extraction and PCR systems, facilitating rapid testing and high-throughput workflows essential for managing infectious diseases [50]. The scalability and modularity of TLA enable adaptation to small, medium, and large laboratories, ensuring continuous operation with minimal downtime [51].

Applications in research and development fields

Recent advancements in medical laboratory automation technology have markedly impacted scientific research and laboratory procedures [52]. The incorporation of AI and ML algorithms empowers researchers to derive crucial insights from intricate information, resulting in enhanced decision-making and hypothesis formulation [52]. Important advancements include robotic sample handling systems, AI, ML algorithms, and safety improvements. Integrating these technologies has led to improved efficiency, repeatability, and safety in laboratory settings [35]. These advancements have hastened scientific advancements and the development of innovative remedies and cures. The future of laboratory automation technology is expected to involve continued progress in robotics, AI, and microfluidics, as well as potential integration with new disciplines such as synthetic biology and precision medicine.

Applications in public health and large-scale testing centers

Implementing TLA in public health and large-scale testing centers can have substantial benefits, such as instrument consolidation and reducing the number of processing steps and testing personnel, based on a study showing an 86% decrease in discrete processing steps and a 45% reduction in the testing footprint [35]. A case study report of two large hospitals in Saudi Arabia showed that TLA improved technical productivity, with an average 1.4–3.7-fold increase in the number of tests performed per worker in clinical chemistry and serology departments [53]. Additionally, TLA reduces the need for manual labor, enabling redefinitions of job roles within the laboratory workforce and making it ideal for high-volume testing environments or those facing workforce shortages [35, 53]. Public health laboratories may have different risk priorities in the pre-analytical process. Risk management based on predefined QIs can reduce risk levels and improve QI performance, serving as evidence-based examples for continuous improvement of pre-analytical processes, with TLA-based pre-analytical quality management offering valuable support [54].

CHALLENGES TO CONSIDER WHEN IMPLEMENTING TLA

When implementing TLA, several challenges should be considered in terms of technical, operational, financial, workforce training, and organizational culture changes (Fig. 3).

Fig. 3. Challenges to consider when implementing total laboratory automation.

Technical barriers

True TLA should integrate the entire workflow, from specimen collection in the phlebotomy room to transportation and reception in the laboratory. Such integration ensures maximal efficiency, minimizes human error, enables accurate specimen tracking, and facilitates rapid testing. Integrating TLA systems with existing LIS and other software poses challenges, as compatibility and seamless data exchange between new automation technologies and current systems are critical for maintaining operational continuity. Installing, maintaining, and troubleshooting TLA systems requires advanced technical expertise and can be challenging for laboratories without specialized knowledge. Effectively managing large volumes of data generated by automated systems and ensuring data security remain key challenges. Proper data handling protocols and robust IT infrastructure are essential for preventing data loss and breaches.

Equipment failures or malfunctions can severely disrupt laboratory operations and productivity. For example, specimen storage, a critical aspect of the post-analytical phase, requires confirmation that automated storage and retrieval modules maintain specimen stability at levels comparable with those of manual storage facilities [55]. Integrity checks for samples (such as those for hemolysis, lipemia, and jaundice), liquid levels, and different types of blood collection tubes (e.g., cap color recognition) are now integrated into most TLA systems. Recent research has focused on implementing sample temperature detection on automation tracks to bypass risks associated with frozen samples being mistakenly loaded into the system [56]. Handling all types of vacuum blood collection tubes through TLA systems presents opportunities and challenges, as different tests require tubes with varying additives, dimensions, and compositions. Automated systems may face difficulties with niche or irregular tube types, including pediatric tubes or tubes requiring specialized handling, such as those for trace element testing or blood cultures [57].

Operational barriers

Effective implementation of new automation systems requires comprehensive training for laboratory staff. Employees must acquire skills needed to operate and maintain new systems and software, which can be time-intensive and require ongoing support. Transitioning to TLA represents a substantial shift in laboratory operations. Change management strategies are essential for helping staff adapt to new processes and technologies, minimizing resistance, and ensuring a seamless transition. TLA adoption may often require a complete redesign of existing laboratory processes, including workflow, role distribution, and sample handling procedures [57]. This restructuring can be complex and time-consuming.

Ensuring QC and regulatory compliance in automated processes requires additional operational effort and oversight. Continuous monitoring and validation of automated systems are necessary for maintaining high standards. For example, assessments are necessary to confirm whether refrigerated storage modules connected to TLA lead to higher serum sample stability than conventional refrigeration systems and whether automated storage systems can optimize workflow and laboratory resources to achieve improved sample stability [58]. Many users prefer integrating third-party measuring instruments into TLA systems to connect various analyzers rather than relying solely on the measuring instruments provided by the TLA system manufacturer. High-volume laboratories, such as reference, core, and clinical trial laboratories, may require customized automation solutions tailored to specific operational needs. Implementing efficient tube-sorting automation in reference and tertiary care laboratories is particularly challenging due to variability in tube types and collection processes among laboratories.

Financial barriers

The initial costs for TLA systems, including equipment and infrastructure modifications, are substantial [59, 60]. These expenses include automated analyzers, sample transport systems, and integrated middleware for data management. Installing TLA often requires laboratory space remodeling, which adds to capital expenditure. This process includes ensuring an adequate power supply, networking capabilities, and physical space requirements. Ongoing costs for maintenance, upgrades, and consumables associated with TLA systems contribute to the financial burden, necessitating careful planning for recurring expenses.

Evaluating the return on investment is essential. Laboratories need to consider the initial and operational costs against the potential savings and efficiency gains offered by TLA. Predictive analytics and workflow optimization provided by TLA systems help reduce waste and manage inventory more effectively. Ongoing support agreements with vendors are necessary to maintain system uptime. These agreements often include software upgrades, remote monitoring, and emergency repairs, which increase the total cost of ownership. As laboratory workloads grow, scalability becomes critical, and future upgrades and expansions must be included in financial planning. Larger TLA models require higher levels of maintenance than for manually operated instruments [61]. Proper risk assessment and mitigation strategies are crucial for managing these challenges.

Organizational culture changes

Comprehensive and ongoing training programs are required to ensure that staff are proficient in operating and maintaining new automation systems. Training should cover technical skills, troubleshooting, and best practices for using automated equipment. As automation takes over routine tasks, staff may need to develop new skills, such as data analysis, system management, and QC, to maintain relevance in a highly automated environment.

Resistance to change may arise due to employee concerns about job loss or unfamiliarity with new technologies. Addressing these concerns through clear communication, support, and involvement during the transition process is vital [62]. Shifting to a culture that values automation and continuous improvement is essential. This requires leadership commitment, fostering a mindset of innovation, and encouraging collaboration and adaptability among staff.

Successfully managing organizational change requires engaging stakeholders at all levels, allocating necessary resources, and communicating the benefits of TLA to all employees. Developing a thorough and robust plan at the outset of the automation process is vital for achieving overall success [63]. Ongoing optimization through monitoring key metrics and continuous process improvement is necessary to maximize and maintain the efficiency gains provided by TLA [48].

FUTURE PROSPECTS AND DEVELOPMENT POTENTIAL OF TLA IN CLINICAL LABORATORIES

The opportunity to connect multiple diagnostic specialties to a single track has proven effective in improving the efficiency, organization, standardization, quality, and safety of laboratory testing while also providing a substantial long-term return on investment and enabling staff requalification [60]. TLA in clinical laboratories represents the pinnacle of efficiency, precision, and innovation in medical testing and diagnostics. The future of TLA is expected to advance significantly through key technological and strategic developments. Below, we describe anticipated technological advancements and development possibilities in the TLA field by incorporating the latest scientific and technological innovations.

Integration of AI and ML

Industry 4.0 has demonstrated significant technological advancements that can enhance laboratory automation efficiency and scalability [64]. AI and ML are set to revolutionize TLA by enhancing diagnostic accuracy, predictive analytics, and workflow optimization. These technologies can analyze vast datasets to identify patterns and anomalies that may be missed by human observers. Adopting AI and ML approaches to analyze real-time data coming from instruments, electronic medical records (EMRs), patient-based real-time QC and quality assurance systems, and feedback from the instrument are essential for eliminating weak links in the TLA chain [32, 65]. AI and ML algorithms support continuous process improvement by predicting equipment malfunctions and optimizing maintenance schedules, thus reducing downtime. Real-time AI-powered feedback loops can dynamically adjust QC processes to adapt to changing variables, thereby minimizing errors and ensuring consistent performance across all testing phases [64]. The interoperability between AI systems and TLA frameworks ensures seamless data flow, enhancing both clinical outcomes and operational efficiency [66]. With the aid of AI, numerical and imaging data can be incorporated into a TLA system and developed into an automated reporting system [9]. Currently, ML models are applied to different phases of the clinical laboratory testing process [67]. The application of AI technology in the pre-analytical, analytical, and post-analytical phases of TLA is summarized in Table 3.

Table 3. AI applications in TLA.

AI technology	Application phase	Example applications	Key benefits
Machine learning	Pre-analytical	Automated sample labeling and sorting Identification of unsuitable samples (e.g., hemolyzed, lipemic)	Reduced error rate Improved processing speed
Predictive analytics	Analytical	Predictive maintenance of equipment Prioritization of sample analysis for critical patients	Increased uptime of instruments Reduced turnaround time
Internet of Things	Pre-analytical and analytical	Real-time monitoring of equipment and samples Automated sample transport systems (robots and conveyors)	Minimized sample transport time Optimized workflow
Deep learning	Analytical	Imaging data analysis (e.g., blood smear evaluation and cell morphology) Detection of complex patterns	Enhanced accuracy Reduced workload for specialists
Natural language processing	Post-analytical	Automatic generation of test interpretation reports Clinical pattern identification in laboratory data	Accelerated result delivery Improved communication accuracy
Robotic process automation	Analytical and post-analytical	Automation of repetitive tasks in equipment calibration Data integration and report generation	Reduced manual intervention Increased processing consistency and reliability

Abbreviations: AI, artificial intelligence; TLA, total laboratory automation.

Advanced robotics and automation systems

The use of advanced robotics will continue to evolve, facilitating more sophisticated and efficient handling of laboratory tasks. These systems can perform repetitive tasks with high precision and minimal error, freeing up human resources for more complex decision-making. Driven by advancements in automation technology, LC-MS/MS equipment is expected to be operated alongside TLA in clinical chemistry laboratories in the near future, reducing and simplifying many of the testing processes that currently require direct human involvement [26]. Robotics also ensures greater standardization across testing phases by enabling high-throughput and error-free sample preparation, fostering faster diagnostic reporting.

IoT connectivity

IoT technology will play a pivotal role in advancing TLA systems by creating seamlessly interconnected laboratory environments. Smart devices, sensors, and equipment will facilitate real-time monitoring, automated data collection, and proactive alerts, markedly enhancing operational efficiency, QC, and workflow optimization. IoT-enabled laboratories can also collect supplementary contextual data, such as environmental conditions or equipment status, which are often overlooked. This enriched data stream enables more accurate root cause analysis, predictive maintenance, and effective troubleshooting, ultimately driving continuous process improvements and ensuring the reliability of automated workflows [68].

Enhanced data analytics and big data

Big data technologies, through their efficient handling and storage of large quantities of data, offer significant advantages to laboratories. They also reveal actionable insights from complex datasets, which can lead to improved clinical outcomes and enhanced operational efficiency. ML and AI algorithms embedded in big data platforms help laboratories identify trends, detect anomalies, and optimize resource management in real time. Predictive analytics further facilitate proactive QC and equipment maintenance, reducing downtime and minimizing errors. Big data solutions support personalized medicine by enabling more precise diagnostics and treatment pathways based on patient-specific data patterns [66].

Cybersecurity and data privacy

When TLA systems integrate seamlessly with the hospital information system, EMRs, and laboratory test results via middleware, they enable the aggregation of diverse patient data, improving clinical decision-making and operational efficiency. However, this interconnected infrastructure introduces significant cybersecurity challenges. The exchange of sensitive health data across multiple systems creates vulnerabilities to unauthorized access, data breaches, and cyberattacks, potentially compromising patient privacy and data integrity [69]. Ensuring robust cybersecurity protocols, such as data encryption, access control mechanisms, and real-time threat monitoring, is essential for maintaining the confidentiality, integrity, and availability of healthcare information within these integrated systems. Addressing the challenges associated with cyberbiosecurity vulnerabilities requires thoughtful design and implementation by equipment manufacturers, software and control systems developers, and by end users [70].

Sustainable and green laboratory practices

Environmental, social, and governance (ESG) principles are becoming increasingly important in healthcare, including laboratory medicine. Applying ESG principles to TLA can address sustainability challenges by focusing on reducing carbon footprints, minimizing waste, and optimizing energy usage. From a governance standpoint, transparent data management and compliance with ethical standards in automated workflows are critical. Social considerations include improving work environments by easing manual workloads and enhancing staff well-being through efficient automation. By implementing energy-efficient systems and integrating eco-friendly technologies and practices, such as minimizing laboratory waste through recycling systems, the environmental impact of laboratory operations will be reduced.

CONCLUSIONS

In this review, we highlighted how TLA is transforming clinical laboratories by enhancing efficiency, accuracy, and operational performance. By automating the entire testing process, TLA shortens TATs, minimizes human errors, and optimizes workflows across the pre-analytical, analytical, and post-analytical phases. We discussed the integration of AI, ML, IoT, and robotics, which are driving advancements in diagnostics and treatment monitoring. Key players such as Abbott, Roche, Siemens, and Beckman Coulter are leading the market with innovative TLA solutions, addressing labor shortages and increasing test volumes. While TLA offers substantial benefits, challenges remain, such as cybersecurity, data management, financial investments, and workforce training. Continuous technological improvements and interdisciplinary collaboration will be crucial for overcoming these challenges and ensuring sustainable, future-ready laboratory operations.