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Artificial Intelligence in Health AI in pharma: Embracing transformation

targets. 18,19 Thereafter, AI could help predict the safety of Such information may assist in resource allocation, risk
potential drugs and facilitate clinical trials by assisting with mitigation, and business performance evaluation.
their design and recruitment strategies. With the trend
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toward personalization and precision medicine, AI and 3.3. Field sales interactions
pharmacogenomics could potentially optimize treatment Relationship-building is vital for building brand loyalty
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for individuals. In fact, Google Cloud has launched and driving sales. While the role of the pharmaceutical
AI tools to provide such support to pharmaceutical representative is promotional, it involves more than simply
companies. 22 selling products, as there is an educational component.
3. Commercial Sales representatives are discouraged from operating
in silos, and it has been recognized that regular training
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AI can provide additional value to the commercial aspects underpins individual performance. Today, the ubiquity
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of the pharmaceutical business by assisting with marketing of smartphones allows sales representatives to record
and sales. professional interactions, provided that consent has been
3.1. Promotional campaigns granted. This allows AI to analyze discussions, share tailored
coaching advice, and empower professional development.
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Promotional materials are essential in establishing As sales representatives must be cognizant of the concerns
credibility and raising awareness of pharmaceutical voiced by healthcare professionals, AI may compile
products. However, their development process can be concerns and cluster insights to provide management with
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time-consuming and requires creativity to maximize an understanding of strategic issues at a regional, national,
clarity, memorability, and appeal. or international level. Furthermore, AI could guide future
Generative AI can generate high-quality image and interactions by suggesting the optimal timing and mode of
text outputs in a relatively short timeframe, given the communication for sales representatives to follow-up with
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correct prompts. Utilizing such tools could assist in-house clients, in accordance with client preferences. 34
marketing teams with brainstorming and branding
and reduce their reliance on external digital marketing 4. Challenges and risks
agencies, thereby improving organizational efficiency. As AI becomes more sophisticated and prevalent, the
Generative AI plug-ins may also be leveraged to produce need for transparency, accountability, and equity becomes
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content personalized to recipients based on the data held increasingly noteworthy. Therefore, it is crucial to address
by the organization; this would further boost engagement regulatory and ethical issues to mitigate potential risks
and impact. Evidently, generative AI represents a sizeable effectively.
economic opportunity, and with competing offerings from
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the leading technology companies, many organisations 4.1. Accuracy
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have assembled taskforces for generative AI. Inaccuracy is a major concern that, if not addressed,
3.2. Market insights could significantly limit the versatility of AI technologies.
There is a legitimate concern that AI contributes to the
Market analysis is crucial for pharmaceutical companies spread of misinformation through “hallucination.” 35(p3)
to identify expansion opportunities, assess competition, In addition, bias could be perpetuated due to AI being
and guide future product development. However, there is a trained on data with inherent biases, which could unjustly
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continual need to stay up-to-date with industry trends and marginalize specific groups. Handling unstructured data
developments owing to the rapidly changing nature of the requires careful consideration, particularly implications
pharmaceutical landscape. from a safety perspective; validating the claims of patient-

AI and data science may provide useful insights into generated information becomes increasingly important.
customer segmentation and communication preferences, 4.2. Data
which could help target messaging and optimize
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engagement. Network analysis could also be utilized to Data usage by AI systems raises additional concern,
examine business prospects, identify influential figures drawing attention to the importance of data protection
within specific niches, and understand their circle of and privacy, especially since cyberattacks pose a growing
influence. Predictive modeling could subsequently threat to organizations. With the abundance of
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assimilate various activities, internal and external, proprietary information in the pharmaceutical industry,
to the organization so as to direct strategic decision- organizations are at risk of major supply chain disruptions
making by forecasting market competition and growth. if sensitive information is compromised. Again, this
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Volume 1 Issue 3 (2024) 3 doi: 10.36922/aih.2973