From identifying cancer in medical images to discovering new antibiotics, artificial intelligence (AI) is reshaping the life sciences. However, as its influence grows, so do the concerns about data bias, transparency, reliability, and ethical responsibility.
This article examines the limitations of AI in life sciences, focusing on challenges such as data bias, model interpretability, ethical concerns, and regulatory hurdles while exploring research efforts aimed at improving AI’s reliability and efficiency in life science research.
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AI's Promise vs. Reality
Artificial intelligence is increasingly being considered a transformative force in life sciences, offering breakthroughs in diagnostics, drug discovery, and personalized medicine.
Yet, despite its promise, AI has not seamlessly revolutionized healthcare as many anticipated. Instead, its implementation is hindered by technical limitations, ethical concerns, and the inherent complexity of biological systems.
While AI has demonstrated capabilities surpassing human expertise in narrow applications, such as imaging-based diagnostics and analyzing large data, its broader integration into healthcare and life sciences remains constrained by issues of data bias, interpretability, and the "black-box" nature of many machine learning models.1-4
Therefore, it is becoming evident that the enthusiasm surrounding AI in medicine must be tempered with a realistic assessment of its limitations. AI is not a panacea for all medical challenges; rather, it is a tool that must be carefully developed, validated, and deployed.
Understanding these constraints is crucial for researchers, clinicians, and policymakers seeking to maximize AI’s benefits while mitigating its risks.2
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Challenges and Limitations
One of the most pressing challenges in AI applications for healthcare is data bias. AI models are only as good as the data they are trained on.
If that data reflects existing biases, such as disparities in access to healthcare or underrepresentation of certain populations, the model will perpetuate these inequalities.
For example, electronic health records (EHRs) can contain biases that affect machine learning predictions. If certain patient groups are overrepresented or underrepresented in training datasets, the resulting AI models may yield skewed outcomes, leading to potential disparities in diagnosis and treatment recommendations.3
Another fundamental challenge is the interpretability of AI models. Many deep learning algorithms operate as "black boxes," meaning that even their developers struggle to explain how specific decisions are made.
In healthcare, where clinical decisions can have life-or-death consequences, a lack of transparency is unacceptable and often dangerous.4 Researchers argue that interpretable AI should be prioritized in applications such as embryo selection and image-based diagnoses, where clear justifications for AI-driven recommendations are essential.5
The Importance of AI in life sciences
Case Examples in Drug Discovery and Diagnostics
AI has demonstrated notable success in drug discovery, but its impact is constrained by the complexities of biological systems and the need for extensive experimental validation. While AI can analyze vast datasets to identify promising drug candidates, these predictions must be rigorously tested in laboratory and clinical settings.
Studies have shown that despite AI-driven advances in antibiotic discovery, millions still die annually from infections, partly due to the unpredictable nature of conditions such as sepsis.
Furthermore, while AI has shown promise in speeding up the identification of potential new antibiotics, it has not yet provided a complete solution to the growing crisis of antimicrobial resistance and the threat posed by superbugs. Researchers argue that the challenge lies not only in discovering new drugs but also in addressing the complex factors that drive resistance development and spread.6
Similarly, AI has been integrated into diagnostics, particularly in point-of-care testing, where it has proved invaluable in optimizing diagnostic workflows, including the computational enhancement of multiplexed assays to improve biomarker detection.
While AI can increase the sensitivity and specificity of diagnostic tools, challenges such as false positives, false negatives, and the need for robust clinical validation persist. Additionally, AI’s ability to detect low-abundance biomarkers remains a limitation.7
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Technical and Ethical Concerns
The widespread adoption of AI in life sciences raises critical technical and ethical concerns. Given the increasing reliance on sensitive patient data to train AI models, data privacy and security are major concerns. Furthermore, ensuring compliance with data protection regulations while enabling the training of AI models using meaningful data remains a significant challenge.1
The issue of accountability is particularly pressing because when AI systems make errors, determining responsibility, whether it lies with developers, clinicians, or regulatory bodies, remains an unresolved dilemma.2
Researchers have also emphasized the necessity of a robust governance framework to regulate AI deployment in healthcare. Such a framework should include ethical principles, transparency requirements, and international cooperation to ensure AI’s benefits are equitably distributed while minimizing risks.1
Another pressing concern is the potential for AI to reinforce existing health disparities. Studies have demonstrated that commercial AI algorithms used in healthcare decision-making have exhibited racial biases, leading to unequal allocation of medical resources.
Addressing these disparities requires proactive efforts to design more equitable AI models, incorporating diverse training data, and implementing fairness-aware algorithms.8
Moreover, the risk of AI-induced automation bias — where healthcare professionals overly rely on AI-generated recommendations without critical assessment — raises ethical questions about the balance between human expertise and machine intelligence.
Therefore, AI’s role should be complementary to human judgment rather than a replacement, along with rigorous oversight and continuous monitoring of AI-driven decisions in clinical settings.9
Lastly, regulatory challenges complicate the adoption of AI in healthcare. Current approval pathways for AI-based medical tools often struggle to keep pace with rapid technological advancements.
The United States (U.S.) Food and Drug Administration (FDA) and the European Medicines Agency (EMA) are working to establish adaptive regulatory frameworks that ensure AI-driven innovations remain safe, effective, and aligned with ethical principles.
However, the dynamic nature of AI models — particularly those capable of self-learning — poses unique challenges for regulatory bodies tasked with balancing innovation and patient safety.10
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Research Gaps and Future Improvements
Despite its current limitations, AI holds immense potential for advancing life sciences. Research is actively addressing key shortcomings, including efforts to improve the interpretability of AI models.
Explainable AI (XAI) methodologies are being developed to enhance transparency and provide clear justifications for AI-generated decisions.
Additionally, ongoing work aims to mitigate data bias through the use of more representative datasets and fairness-aware algorithms. In diagnostics, researchers have suggested that AI-enhanced point-of-care testing devices could be refined to improve analytical sensitivity and ensure more accurate biomarker detection.7
For AI to fulfill its promise in life sciences, it must be implemented responsibly. By acknowledging its limitations regarding bias, interpretability, and ethical concerns, researchers and industry professionals can work toward developing AI solutions that are both effective and equitable.
The future of AI in healthcare depends not on uncritical enthusiasm but on rigorous research, thoughtful regulation, and continuous refinement of its methodologies.
References
- Mennella, C., Maniscalco, U., De Pietro, G., & Esposito, M. (2024). Ethical and regulatory challenges of AI technologies in healthcare: A narrative review. Heliyon, 10(4), e26297. DOI:10.1016/j.heliyon.2024.e26297
- Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future healthcare journal, 6(2), 94–98. DOI:10.7861/futurehosp.6-2-94
- Momenzadeh, A., Shamsa, A., & Meyer, J. G. (2022). Bias or biology? Importance of model interpretation in machine learning studies from electronic health records. JAMIA open, 5(3), ooac063. DOI:10.1093/jamiaopen/ooac063
- Petch, J., Di, S., & Nelson, W. (2022). Opening the Black Box: The Promise and Limitations of Explainable Machine Learning in Cardiology. The Canadian journal of cardiology, 38(2), 204–213. DOI:10.1016/j.cjca.2021.09.004
- Afnan, M. A. M., Liu, Y., Conitzer, V., Rudin, C., Mishra, A., Savulescu, J., & Afnan, M. (2021). Interpretable, not black-box, artificial intelligence should be used for embryo selection. Human reproduction open, 2021(4), hoab040. DOI:10.1093/hropen/hoab040
- Cesaro, A., Hoffman, S. C., Das, P., & de la Fuente-Nunez, C. (2025). Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance. npj antimicrobials and resistance, 3(1), 2. DOI:10.1038/s44259-024-00068-x
- Han, G., Goncharov, A., Eryilmaz, M., Ye, S., Palanisamy, B., Ghosh, R., Lisi, F., Rogers, E., Guzman, D., Yigci, D., Tasoglu, S., Carlo, D., Goda, K., McKendry, R. A., & Ozcan, A. (2025). Machine learning in point-of-care testing: innovations, challenges, and opportunities. Nature communications, 16(1), 3165. DOI:10.1038/s41467025585276
- Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science (New York, N.Y.), 366(6464), 447–453. DOI:10.1126/science.aax2342
- Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021). The false hope of current approaches to explainable artificial intelligence in health care. The Lancet. Digital health, 3(11), e745–e750. DOI:10.1016/S2589-7500(21)00208-9
- Topol E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature medicine, 25(1), 44–56. DOI:10.1038/s41591-018-0300-7
Further Reading