Global shortage of AI talent continues to be a serious challenge for the biopharma industry, repeating the trend from our previous reports. While big pharmaceutical companies invest substantial capital in recruitment of AI specialists, still the majority of them are acquired by large tech corporations (Google, Amazon, Alibaba, Tencent, Baidu etc.)  However, a growing wave of specialized university programs and courses, geared towards data science and AI application, is projected to address this issue to certain extent in the coming years.

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Lack of available quality data is still a challenge for the unleashing full potential of deep learning technologies. Numerous variations of deep learning (DL) are believed to be the most lucrative area of AI for applications such as drug discovery and clinical research. The key challenge is that DL algorithms are “data-greedy”, while big data in biotech is not always well-versed for modeling, or is inaccessible due to privacy reasons. 
 

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Ethical, legal, and regulatory issues for AI adoption in the pharmaceutical sciences. This set of challenges is related to the previous point, but also includes other questions -- AI explainability, patentability of AI-generated results, non-optimal regulations in various countries, slowing down the progress and adoption of AI technologies in general, and in the pharmaceutical industry in particular.