How Will Systematic AI Use Impact the Biotech Industry?

By the end of 2025, how much will AI impact our daily “wet lab” tasks? 

25%, 50%, 70%? 

On the one hand, AI could lead to more accurate data, which may lead to increased rationale for developing biotech and pharma solutions and, hence, more wet lab work. On the other hand, AI may be so predictive that the need for wet lab work may diminish. In the short term, there is likely to be a balance between these two scenarios depending on the AI application. Regardless, outsourced data generation and analysis and many companies looking to save money on wet lab work are increasing the focus on implementing a strong, foundational, and systematic Digital Lab Strategy. Ultimately, many labs will have to make a digital decision about their level of digitalisation and implementation of systematic AI.

Digital Decision with Systematic AI

People are putting more and more trust in the data being provided to us, mainly if they trust the data source. For example, Google is trusted by most Americans. From Googling a question and reading the first 2-3 lines of a search result, many people browsing will accept this content as truth. For deep machine learning and analysis, which maps data and analyses thousands of interconnected data points, trust is being built and requires testing and validation. 

Labs that invested in structuring their data years ago are now in the perfect position for exponential growth. New labs, starting out, have a great opportunity to set a strong foundation that not only will leverage the data that is being collected, but can and will create countless integrations with desired databases, robots, and additional highly specialised software.

What is Systematic AI?

Systematic AI is the organised and structured approach to developing and implementing artificial intelligence (AI) systems. It involves carefully planning, executing, and evaluating AI initiatives to achieve specific goals or solve particular problems in a systematic manner.

Key Aspects of Systematic AI

For labs implementing systematic AI, following this 8-step organised process can ensure you get the most out of your efforts:

1. Problem Definition: Systematic AI begins with clearly defining the problem or objective the AI system intends to address. This involves understanding the domain context, identifying stakeholders’ needs, and establishing measurable goals and success criteria.
2. Data Acquisition and Preparation: Data plays a crucial role in AI systems. Systematic AI involves acquiring relevant data from diverse sources, ensuring its quality, and pre-processing it to make it suitable for AI model training.
3. Algorithm Selection and Development: Systematic AI involves selecting appropriate AI algorithms or methodologies based on the problem requirements and data characteristics. This may include experimenting with different algorithms, tuning parameters, and optimising performance.
4. Model Training and Validation: Once the algorithm is selected, systematic AI entails training the AI model on the prepared data and validating its performance using appropriate evaluation metrics. This may involve techniques such as cross-validation, holdout validation, or bootstrapping.
5. Deployment and Integration: Systematic AI involves deploying the trained AI model into production environments and integrating it into existing systems or workflows. This may require collaboration with IT teams, software engineers, and domain experts to ensure seamless integration and functionality.
6. Monitoring and Maintenance: Systematic AI includes establishing mechanisms for monitoring the performance of deployed AI systems and maintaining their functionality over time. This may involve monitoring data drift, model degradation, and system performance to identify and address issues proactively.
7. Ethical and Regulatory Considerations: Systematic AI requires consideration of ethical and regulatory implications throughout the AI development lifecycle. This includes ensuring fairness, transparency, accountability, and privacy in AI systems and complying with relevant regulations and standards.
8. Continuous Improvement: Systematic AI involves continuously iterating and improving AI systems based on feedback, new data, and evolving requirements. This may involve re-training models, updating algorithms, or re-designing systems to address changing needs and improve performance.

By following a systematic approach, organisations can effectively leverage AI to address complex challenges, drive innovation, and achieve desired outcomes in various domains, including healthcare, finance, manufacturing, and more.

Systematic AI In Biotech and Life Sciences

Systematic AI in biotech and the life sciences refers to the holistic integration of artificial intelligence (AI) technologies across various aspects of research, development, and application within these domains. It involves leveraging AI methodologies and techniques to address complex challenges and opportunities in drug discovery, personalised medicine, genomics, bioinformatics, and biomedical imaging. 

Here’s how systematic AI is manifesting in the life sciences and biotech:

• Drug Discovery and Development: Systematic AI in drug discovery involves utilising AI algorithms for various tasks, including virtual screening, target identification, lead optimisation, and pharmacological modelling. By analysing large-scale biological and chemical data, AI can expedite the drug discovery, identify promising drug candidates, and optimise therapeutic efficacy while minimising adverse effects.
• Personalised Medicine: Systematic AI enables the development of personalised medicine approaches tailored to individual patient characteristics, such as genetic makeup, molecular profiles, and clinical history. AI algorithms analyse patient data to predict disease susceptibility, stratify patient populations, optimise treatment regimens, and identify biomarkers for early diagnosis and prognosis.
• Genomics and Bioinformatics: In genomics and bioinformatics, systematic AI facilitates the analysis and interpretation of genomic data, including DNA sequencing, gene expression profiling, and genetic variation analysis. AI algorithms can identify genetic variants associated with diseases, characterise gene regulatory networks, and predict functional consequences of genetic mutations, advancing our understanding of complex biological systems.
• Biomedical Imaging and Diagnostics: Systematic AI enhances biomedical imaging and diagnostics by enabling automated image analysis, feature extraction, and pattern recognition in medical images. AI-powered imaging techniques, such as deep learning-based image classification and segmentation, improve diagnostic accuracy, enable early disease detection, and guide treatment planning in radiology, pathology, and medical imaging.
• Biological Data Integration and Analysis: Systematic AI integrates diverse biological datasets, including genomic, transcriptomic, proteomic, and metabolomic data, to uncover underlying molecular mechanisms and biological pathways. AI-driven approaches, such as network analysis, pathway enrichment, and multi-omics integration, elucidate complex interactions within biological systems, identify novel therapeutic targets, and predict drug responses.
• Clinical Decision Support Systems: Systematic AI develops clinical decision support systems that assist healthcare professionals in diagnosing diseases, predicting patient outcomes, and optimising treatment decisions. AI algorithms analyse patient data from electronic health records, medical imaging, and wearable devices to provide evidence-based recommendations, improve healthcare delivery, and enhance patient outcomes.
• Regulatory Compliance and Safety: Systematic AI ensures compliance with regulatory requirements and safety standards in life sciences and biotech applications. AI-driven tools assist in regulatory submissions, adverse event monitoring, pharmacovigilance, and drug safety assessment, enhancing transparency, accountability, and regulatory compliance throughout the product lifecycle.

Overall, systematic AI in life sciences and biotech leverages AI technologies comprehensively to accelerate scientific discovery, enhance healthcare delivery, and address global health challenges. By harnessing the power of AI across the research, development, and application continuum, systematic AI drives innovation, fosters collaboration, and transforms the future of medicine and biotechnology.

Shortcomings of Systematic AI

Data Quality and Availability

AI algorithms rely heavily on high-quality, diverse, and well-annotated datasets for training and validation. Acquiring such datasets in life sciences and biotech can be challenging due to various factors. First, biological data often exhibit high dimensionality, heterogeneity, and complexity, making it difficult to capture all relevant features accurately. Additionally, datasets may be limited in size or suffer from biases, such as underrepresenting certain populations or experimental conditions. Moreover, accessing proprietary or sensitive data for research can pose legal and ethical challenges. These limitations can impede the development and generalisation of AI models in life sciences and biotech.

Interpretability and Explainability – Mapping the Data

Another significant challenge in applying AI to life sciences and biotech is AI models’ lack of interpretability and explainability. While deep learning techniques, such as neural networks, often achieve state-of-the-art performance in various tasks, they are inherently black-box models, making it challenging to understand the underlying decision-making process. In fields where transparency and interpretability are crucial, such as clinical decision support systems or drug discovery, this lack of explainability hinders the trustworthiness and acceptance of AI solutions. Researchers and regulators require methods to interpret AI predictions, understand feature importance, and identify potential biases to ensure the reliability and safety of AI-driven applications.

Data Standardisation Challenges

One of the key shortcomings of AI in life sciences and biotech revolves around data standardisation issues. Biological data often come from diverse sources, including different laboratories, experiments, and data generation techniques. As a result, the lack of standardised data formats, annotations, and metadata poses significant challenges for AI algorithms. Inconsistent data formats and structures hinder interoperability, making integrating and analysing datasets from multiple sources difficult. 

Moreover, variations in data quality, preprocessing methods, and experimental protocols further complicate the standardisation process. AI models may struggle to generalise across datasets without standardised data, leading to biased or unreliable predictions. Addressing data standardisation challenges requires concerted efforts to develop and adopt standardised formats, ontologies, and metadata schemas for representing biological data. Collaborative initiatives, such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles, aim to promote data standardisation and facilitate data sharing and reuse in the life sciences community. 

By promoting data standardisation, researchers can enhance AI-driven analyses’ reliability, reproducibility, and interoperability in life sciences and biotech, ultimately accelerating scientific discovery and innovation.

Sustainable Path Forward with Systematic AI

To achieve a sustainable and systematic AI implementation in the biotech industry and life science labs in the coming years, several key actions need to be taken:

1. Invest in Data Infrastructure and Standardization: Establish robust data infrastructure to support the storage, management, and sharing of diverse biological datasets. Implement data standardisation protocols, ontologies, and metadata schemas to ensure interoperability and consistency across datasets. Invest in data curation, annotation, and quality control processes to maintain data integrity and reliability.
2. Develop AI Talent and Expertise: Invest in training programs and professional development opportunities to build AI expertise within biotech and life science labs. Offer interdisciplinary training in biology, computational sciences, and AI methodologies to equip researchers with the skills and knowledge necessary to leverage AI effectively. Foster collaboration between biologists, data scientists, and AI researchers to promote knowledge exchange and cross-disciplinary innovation.
3. Promote Ethical and Responsible AI Practices: Establish ethical guidelines and regulatory frameworks for biotech and life sciences AI applications. Ensure compliance with privacy regulations, data security standards, and ethical principles governing AI research and development. Foster a culture of transparency, accountability, and responsible AI use, with mechanisms in place for addressing ethical concerns and mitigating potential biases in AI algorithms.
4. Facilitate Data Sharing and Collaboration: Promote data sharing and collaboration among biotech and life science labs to facilitate data exchange, tools, and methodologies. Establish data-sharing agreements, collaborative research networks, and open-access platforms for sharing datasets, AI models, and research findings. Encourage pre-competitive collaboration and knowledge sharing to accelerate scientific discovery and innovation.
5. Develop Robust AI Solutions for Specific Applications: Invest in developing AI-driven solutions tailored to specific applications and research areas within biotech and life sciences. Focus on addressing domain-specific challenges, such as drug discovery, personalised medicine, genomics, and biomedical imaging, through AI-powered approaches. Collaborate with industry partners, academic institutions, and technology providers to co-develop and validate AI solutions that meet the unique needs of the life science community.
6. Ensure Interoperability and Scalability: Design AI systems with interoperability and scalability in mind to facilitate integration into existing workflows and infrastructure. Adopt open-source standards and modular architectures to enable seamless integration with laboratory automation systems, data management platforms, and analytical tools. Ensure AI solutions can scale to accommodate growing data volumes, evolving research needs, and emerging technologies.
7. Evaluate Performance and Impact: Implement mechanisms for evaluating the performance and impact of AI implementations in biotech and life science labs. Define key performance indicators (KPIs) and metrics to measure AI solutions’ effectiveness, efficiency, and reliability in achieving desired outcomes. Conduct regular assessments, audits, and reviews to identify areas for improvement and optimisation.
8. Promote Technical Diversity: Foster technical knowledge diversity in AI research and development efforts within the labs. Promote diversity in recruiting AI talent, research collaborations, and leadership positions. Ensure representation of diverse perspectives, backgrounds, and expertise to drive innovation.

Summary

By taking these actions, biotech and life science labs can establish a sustainable and systematic AI implementation path forward, enabling them to harness the full potential of AI technologies to accelerate scientific discovery, improve healthcare outcomes, and address global health challenges. If you are interested in learning more about the implementation of AI in your lab and/or company, contact eLabNext here to discuss how we can help.