The SDLC of GenAI in the Life Sciences: SDLC Realms and SDLC Realities

Companies operating under life sciences are facing more pressure to be more innovative and operate within some of the most complex operating environments in the world. The software systems have to handle sensitive patient information, comply with changing regulatory systems and promote mission-based functions such as clinical trials, medical equipment development, pharmacovigilance and laboratory functions without jeopardizing compliance and quality. However, life sciences are notoriously slow and expensive with its software development life cycle (SDLC). The requirements are tight, the requirements are volatile, the test is exhaustive, the documentation requirement is overwhelming and validation time can add months to the schedule. Enter Generative AI (GenAI). GenAI is starting to have a physical presence in SDLC life sciences organization. We will highlight the largest points of opportunity, but we will base each one on reality, enquiring of how it is working in practice today and what obstacles still exist. The trick, of course, as ever is to strike the right balance between innovation and compliance and to find the right partners to enable you to balance them.

The Problem

Life sciences teams waste a lot of time in converting intricate regulatory frameworks (FDA 21 CFR Part 11, good practice guidelines (GxP), GDPR, HIPAA) into software specifications. Even small misunderstandings can result in reworking and unsuccessful audits that are expensive to fix.

The rationale behind GenAI

GenAI is capable of quickly processing regulatory documents, clinical procedures and industry guidelines, and write straightforward, structured sets of requirements. It is able to identify possible areas of compliance lapses prior to the commencement of development. This minimizes the grey area, shortens the design stage and assists in making sure that the solutions are designed to be compliant and not remodeled to be tested.

Reality Check

This is effective with controlled pilots, but relies on quality input data and fine-tuning which depends on domain knowledge. When the AI is trained using the incomplete or outdated regulatory materials, it might produce an erroneous or excessively generic output. Up to now, the vast majority of organizations have applied GenAI as a base but not as a substitution of human regulatory knowledge.

The Issue

Life sciences institutions tend to have disjointed systems, such as clinical trial management platforms, lab information systems, enterprise resource planning (ERP) systems and regulatory submission systems, which seldom have conversations with each other. The developers will have to work extra hard to integrate the legacy systems and still have validated environments.

Why GenAI

AI-based code assistants make development faster by recommending context-specific code, making API integrations and even refactoring legacy code to perform better and be more maintainable. GenAI can be modified with best practices in the industry to meet the validation and security requirements in very regulated environments.

Reality Check

Coding assistance has become one of the more successful GenAI applications, yet there are challenges of integration. The legacy systems in life sciences are usually customized and lack documentation of source and thus the AI-generated code is challenging to test or execute without a human audit.

• It takes a chance of black box results too - the teams do not necessarily know why the AI is recommending a certain coding
• This can be an issue with auditability
• GenAI has enhanced productivity in the normally coded duties
• Validated code that is essential to the mission must be monitored entirely

The Issue

Life sciences testing does not merely involve functionality, but involves making sure that all the features are compatible with safety and regulatory requirements. Validation protocols and audit trails also eat an unwarranted share of resources and time, as well as user acceptance testing (UAT).

Why GenAI

The GenAI can produce detailed test scripts (and even test data), act as a simulated real-world laboratory or clinical environment and provide a trace between test outcomes and requirements. It can identify flaws in the coverage of testing and validation, which is essential in the preparation of FDA or EMA inspection. This minimizes the chances of non-conformity and accelerates testing periods.

Reality Check

Test generation automation is suitable to functional testing, but it is not suitable to situations of subtle validation where patient safety or regulatory adherence is at risk. AI-generated test cases often need extensive review to ensure they align with GxP standards.

The Problem

The life sciences SDLC documentation is the blood and life force, as well as the bottleneck, of documentation. Each change will necessitate new traceability matrices, validation reports and standard operating procedures (SOPs). Manual documentation does not only slow progress, but also it enhances the chances of errors.

Why GenAI

GenAI is able to automatically create and keep compliance-ready documentation - such as validation protocols and audit-compliant change logs - to save hours and hours of manual labor. It facilitates keeping documents in line with the real state of the system by combining with the already existing quality management systems, and so the regulatory audits become painfully less.

Reality Check

Documentation automation has potential but has had issues with contextual accuracy such as the correct amount of detail to provide in particular regulatory authorities or with formatting and style that precisely matches that of submissions.

The Dilemma

Companies in the life sciences can not afford to break fast and break things. They need to be innovative in a responsible manner that does not compromise patient data, auditability, and ethics in using AI. A lot of them have a problem with the models of governance enabling the adoption of GenAI without creating a risk factor.

GenAI why

GenAI does not eliminate the necessity of powerful governance, but it can be used to help in enforcing policies. As an illustration, it is capable of raising an alarm over possible data privacy breaches, assist in generating risk evaluations and supplying explainable results to enable auditability. Together with a well-developed oversight system, GenAI can be seen as a source of responsible innovation and, therefore, not a risk factor.

Reality Check

This is one of the most difficult areas to get correct. The field of AI governance in life sciences remains young - numerous companies do not have clear policies on model training data, auditability and explainability. Primary risks include:

• Data leakage (utilizing sensitive patient data as a training model)
• Regulatory uncertainty (AI-generated outputs can cast doubt on the authorship and responsibility)