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Introduction

Drug development has always required patience. It is a process shaped by uncertainty, long timelines, and the constant challenge of translating scientific discovery into real world treatment. Even with careful planning, many promising therapies fail before they reach patients.

Now, artificial intelligence is beginning to change that process.

It can analyse vast datasets within seconds, identify patterns that would take years to uncover, and guide decisions across discovery, testing, and safety monitoring. What once depended heavily on human interpretation is increasingly supported by algorithm driven insight.

This shift brings clear advantages. At the same time, it introduces a more subtle and important question.

As AI becomes more involved in decision making, what happens to the role of human judgment?

The Complexity of Drug Development

Developing a drug is not simply a scientific process. It is an exercise in uncertainty, where thousands of compounds are explored, yet only a small number progress to clinical trials, and even fewer are ultimately approved.

This high failure rate reflects the complexity of human biology, where responses to treatment vary widely and outcomes are not always predictable.

Artificial intelligence offers a way to manage this complexity by identifying patterns within large datasets, helping researchers make more informed decisions earlier in the process. However, while it improves efficiency, it does not eliminate uncertainty.

Where Artificial Intelligence Is Changing the Process

Artificial intelligence is now integrated across multiple stages of drug development, from early discovery to post-market monitoring. Its role becomes particularly significant during clinical trials, where decisions have direct implications for both patient safety and regulatory approval.

Artificial Intelligence in Clinical Trials

Clinical trials remain essential for determining whether a drug is safe and effective. However, they are often limited by delays in recruitment, variability in patient response, and the complexity of study design.

Artificial intelligence is helping address these challenges by reshaping how trials are designed, conducted, and interpreted.

During the design phase, AI analyses data from previous studies and large patient datasets to refine inclusion criteria, determine appropriate sample sizes, and identify meaningful endpoints, allowing trials to be more targeted and efficient.¹

Patient recruitment, which is one of the most common causes of delay, is also improved through AI. By analysing electronic health records and clinical data, algorithms can identify individuals who meet specific criteria more accurately and efficiently, enabling faster enrolment and more appropriate participant selection.¹

AI also builds on existing drug research, including biochemical properties, pharmacokinetics, and known mechanisms of action, and combines this with real world patient data. By linking these datasets, it can more precisely match therapies to biological targets and patient populations, supporting a more personalised and focused trial approach.²

As trials progress, AI enables real time monitoring of patient data, where it can detect patterns in treatment response or early signs of adverse effects, allowing timely adjustments that improve both safety and outcomes.³

In addition, AI contributes to the interpretation of trial results by identifying trends and predicting outcomes based on complex datasets, which can influence decisions on whether a drug progresses, is modified, or is discontinued.²

While these capabilities improve efficiency and precision, they also increase the influence of algorithm generated insights within decision making processes.

When Efficiency Changes How Decisions Are Made

As AI becomes more reliable and widely used, the way decisions are made begins to shift.

Traditionally, clinical research has relied on a balance between statistical analysis and clinical judgment. Data is interpreted carefully, questioned where necessary, and always considered within the broader clinical context.

With AI, much of this interpretation is pre processed. Outputs are structured, confident, and often presented as clear recommendations. While this improves efficiency, it can also make results easier to accept without deeper evaluation.

The concern is not that AI replaces clinicians, but that it changes how actively their judgment is applied.

When decision making becomes more dependent on algorithmic outputs, there is a risk that critical evaluation becomes less prominent, which may reduce the depth of clinical reasoning applied to complex situations.

Evidence of Shifting Clinical Decision Making

Emerging evidence suggests that reliance on algorithm based systems can influence how clinicians engage with information.

Studies have shown that clinicians may be more likely to follow AI-generated recommendations, even when there are reasons to question them, a phenomenon known as automation bias.⁴ This tendency can reduce independent verification, particularly in high pressure or time constrained environments.

In addition, the use of large datasets introduces the possibility of bias, where patterns identified in the data may not fully represent diverse patient populations.⁵ Without careful clinical oversight, these limitations may not always be recognised.

These findings do not suggest that AI is ineffective, but rather that its influence can subtly reshape how decisions are approached.

Clinical Perspective

In real clinical practice, patients rarely fit into predefined categories. They present with overlapping symptoms, varying responses to treatment, and clinical situations that require interpretation beyond data alone.

This is particularly evident in resource limited settings, where decisions are often made with incomplete information and require adaptability as well as experience.

AI can support decision making by providing additional insight and highlighting patterns. However, it does not replace the responsibility of interpreting those findings within the context of an individual patient.

Conclusion

Artificial intelligence is transforming drug development by improving efficiency, refining clinical trials, and expanding what is possible within pharmaceutical research.

However, its growing role also changes how decisions are made.

The question is not whether AI should be used, but how it is used.

Because while AI can guide decisions, it does not carry responsibility for them, and as its influence increases, maintaining active clinical judgment becomes essential to ensure that decisions remain not only efficient, but appropriate and patient

centered.

References

1. Harrer S, Shah P, Antony B, Hu J. Artificial intelligence for clinical trial design. Trends Pharmacol Sci. 2019;40(8):577–591.
2. Mak KK, Pichika MR. Artificial intelligence in drug development. Drug Discov Today. 2019;24(3):773–780.
3. Bate A, Hobbiger SF. Artificial intelligence for pharmacovigilance. Drug Saf. 2021;44(7):683–690.
4. Goddard K, Roudsari A, Wyatt JC. Automation bias in electronic decision support systems. J Am Med Inform Assoc. 2012;19(1):121–127.
5. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in health algorithms. Science. 2019;366(6464):447–453.

Written by Dr. Nozithelo Moyo, Medical Doctor and Medical Writer.