5 Diagnostic Development Trends from ADLM 2026

September 3, 2026

Key Takeaways From ADLM 2026

The five trends shaping the future of diagnostic development are:

1. Artificial intelligence is moving into clinical laboratory workflows.
2. Testing is moving beyond the traditional central laboratory.
3. Next-generation biomarkers are becoming more clinically actionable.
4. Accessibility is becoming a core consideration in diagnostic design.
5. Representative clinical cohorts are increasingly essential to validation.

Here is what each development could mean for diagnostic and life science companies.

1. AI in Diagnostics Is Moving From Discussion to Implementation

Artificial intelligence is no longer only a future consideration for clinical laboratories. AI and machine learning are increasingly being evaluated as practical tools for interpreting test results, recognizing complex patterns, improving laboratory operations, and supporting diagnostic decisions.

The question is shifting from whether laboratories will use AI to how developers can demonstrate that an AI-enabled system works reliably.

That requires more than an effective algorithm. Developers must evaluate performance across intended-use populations, disease states, demographic groups, instruments, collection sites, and real-world clinical conditions. Data quality, cohort diversity, and appropriate controls all influence whether results can be considered representative and reproducible.

For AI-enabled diagnostic development, strong validation may depend on:

- Representative case and control populations
- Consistently collected and processed specimens
- Detailed clinical and demographic annotations
- Data from multiple sites and patient populations
- Clearly defined endpoints and performance measures

As AI assumes a greater role in laboratory medicine, access to high-quality specimens and clinical data will remain central to building confidence in its output.

2. Diagnostic Testing Is Moving Beyond the Central Laboratory

Point-of-care testing, mobile platforms, wearable technologies, self-collection, and other decentralized approaches are bringing diagnostic testing closer to patients.

ADLM 2026 highlighted innovations ranging from portable diagnostic technologies to new approaches in cervical cancer screening. These models could shorten turnaround times, simplify sample collection, and extend testing to communities where conventional laboratory infrastructure is difficult to access.

However, moving a test outside the controlled environment of a central laboratory creates additional development and validation considerations.

A decentralized diagnostic may need to perform reliably across:

- Different collection environments
- Multiple specimen types or collection devices
- Variable transport and storage conditions
- Intended users with different levels of training
- Diverse geographic and demographic populations
- Real-world pre-analytical conditions

Developers must therefore consider the complete testing pathway—not only the assay’s analytical performance. Collection, handling, usability, transport, environmental exposure, and patient behavior may all affect the quality of the final result.

3. Next-Generation Biomarkers Are Becoming Clinically Actionable

Another important diagnostic development trend is the continued movement of biomarkers from discovery toward clinical application.

Advances involving Alzheimer’s disease, cancer, circulating tumor DNA, molecular diagnostics, proteomics, and mass spectrometry are opening new possibilities for earlier disease detection and more precise clinical decision-making.

The challenge is translating an encouraging biological signal into a validated diagnostic tool.

As a biomarker program progresses, developers may need specimens that reflect specific disease stages, treatment histories, molecular profiles, comorbidities, or clinical outcomes. A general disease-state sample may not be sufficient when an assay must distinguish among narrowly defined patient groups.

Well-characterized biospecimens can support several stages of this process, including:

- Biomarker discovery and prioritization
- Assay development and optimization
- Analytical verification
- Cutoff establishment
- Case-control studies
- Clinical validation
- Method-comparison studies

The more precise a biomarker’s intended use becomes, the more important it is to build a cohort around the actual research question.

4. Access Matters Alongside Analytical Performance

A diagnostic test may demonstrate excellent sensitivity and specificity, but its clinical impact remains limited if patients cannot readily access or complete it.

Discussions involving cervical cancer screening, mobile testing, self-collection, and decentralized diagnostics reinforced the importance of designing technologies for broader and more diverse patient populations.

The future of diagnostics is therefore not only about making tests more analytically sensitive. It is also about making testing:

- Easier to use
- Faster to complete
- Scalable across care settings
- Suitable for broader populations
- Practical in areas with limited laboratory infrastructure

Accessibility should be considered during development, not only after a test reaches the market. Including relevant populations and real-world collection conditions in clinical studies can help developers identify potential barriers earlier.

Broader access and strong analytical performance are complementary goals. A test must work reliably, but it must also work for the people and in the settings where it is intended to be used.

5. Better Diagnostics Require Better Clinical Cohorts

The growing importance of high-quality, well-characterized clinical cohorts connects all of these trends.

AI models require representative datasets. Biomarker programs need specimens associated with relevant clinical characteristics and outcomes. Diagnostic developers need carefully selected case and control populations. Decentralized technologies must be evaluated using realistic collection methods and conditions.

As diagnostic technologies become more sophisticated, obtaining a specimen with the correct label is no longer enough. Developers increasingly need access to samples accompanied by meaningful clinical, demographic, laboratory, molecular, treatment, and outcome data.

Depending on the study, an effective cohort may require:

- Clearly defined inclusion and exclusion criteria
- Appropriate case and control populations
- Demographic and geographic diversity
- Confirmed disease status
- Treatment and medical histories
- Laboratory or pathology results
- Biomarker or molecular characterization
- Standardized collection and processing
- Longitudinal samples or defined collection time points

When existing inventory cannot satisfy these requirements, a custom prospective collection can create a cohort aligned with the study protocol and intended use of the diagnostic.

Supporting Diagnostic Development From Discovery Through Validation

Boca Biolistics supports diagnostic, IVD, biotechnology, and biopharmaceutical companies with access to research biospecimens, custom prospective collections, clinical research services, and central laboratory testing.

Our capabilities include more than one million ethically collected biospecimens, a global clinical-site network, and integrated testing through a CAP-accredited, CLIA-certified central laboratory.

Whether a program requires existing inventory, a custom cohort, specialized clinical annotations, laboratory testing, or an end-to-end collection strategy, our goal remains the same: to help researchers obtain the samples and data needed to move promising technologies from discovery through validation and toward clinical application.

Contact the Boca Biolistics team to discuss the biospecimen, testing, or clinical cohort requirements for your diagnostic development program.

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