The potential for tremendous growth in Bioscience Artificial intelligence (AI) is staggering with some areas expected to grow as much as 40% Compounded Annual Growth Rate (CAGR) over the next 10 years. Potential areas of growth include: 1). Drug Discovery, 2). Genomics, 3). Medical Imaging, 4). Precision Medicine, 5). Synthetic Biology and 6). Robotics and Automation to name a few. Some leaders are calling AI “the new electricity” of the modern economy.
If this is the case, the next question becomes: What types of NEW skills will be needed by executives in the new bioscience AI economy?
Needed Bioscience Executive Skills
Throughout this article, we will be addressing skills that will be important to this industry as it progresses forward. Keep in mind that this list is not an all-encompassing list. The skills required will depend on other variables such as the size, location, stage, product, competition and depth and breadth of Data Team. Also, keep in mind that the sample size of data for this article was generated primarily by interviews with executives, technologists and investors of emerging high growth Bioscience Technology companies when searching for AI Executives for their companies.
Understanding the basics of AI: to effectively plan and implement AI strategies, bioscience executives must have a foundational understanding of AI technologies and terminology, including: machine learning, deep learning, neural networks, and natural language processing concepts. Understanding the risks, limitations and potential of AI algorithms as they pertain to bioscience are also important.
Familiarity with big data: The ability to work with and manage large biological data sets is critical for AI-based initiatives. Bioscience executives must have a good understanding of big data technologies, tools and identify patterns and relationships that can impact decision making in bioscience research or product development.
Strategic planning: Bioscience executives need to be able to identify potential areas where AI can be applied to enhance business operations or improve patient or drug discovery outcomes. They should also be able to develop a roadmap for AI implementation that aligns with the company’s overall strategy.
Knowledge of biological processes: Executives in the bioscience industry should have a strong understanding of biological processes and how they relate to the development of new products, therapies and processes. They should be able to apply this knowledge to identify opportunities for AI to enhance research and development efforts, as well as to assess the potential impact of AI on existing products and therapies.
Data literacy: Executives should be proficient in data analysis and interpretation, and able to make data-driven decisions based on insights generated by AI systems. They should also be able to communicate these insights effectively to stakeholders across the organization.
Data analysis: Executives should have a strong understanding of data analysis techniques and be able to use them to extract insights from large datasets that will be generated by AI such as genomic data, clinical trial data and patient data. They should be able to identify patterns, trends, and potential areas for innovation or improvement.
Understanding of Machine Learning algorithms: Bioscience executives should have a strong understanding of the various types of machine learning algorithms and their applications in the bioscience industry. They should be able to assess which algorithms are best suited for specific tasks, such as predicting disease outcomes or identifying new drug targets.
Familiarity with AI programming languages and tools: Executives should have a basic understanding of AI programming languages and tools commonly used in AI and machine learning, such as Python, R, and TensorFlow. They should also be able to work with data scientists and software developers to integrate machine learning algorithms into existing systems, processes and applications.
Ethical considerations: As AI becomes more widespread in the bioscience industry, executives should be aware of the ethical considerations surrounding the use of these technologies.
Regulatory compliance: Executives should have a deep understanding of regulatory requirements related to the use of AI in the industry, including those related to data privacy and security. They should be able to work with legal and compliance teams to ensure that AI initiatives comply with all applicable regulations.
Data governance: Executives should understand the importance of data governance and be able to establish policies and processes for data management that ensure data quality, accuracy, and compliance.
Risk management: Executives should have the ability to assess and manage risk associated with the use of AI technologies in the industry. They should be able to identify potential risks related to AI, such as bias, overconfidence, inequities in algorithms, and implement appropriate measures to mitigate these risks. They should also be able to communicate potential risks of prediction-based algorithmic decision making to stakeholders and develop risk management strategies that minimize potential harm to the overall business.
Strong leadership skills: Executives must be able to lead cross-functional teams that include AI experts, data scientists, machine learning engineers, and business leaders, and must be able to communicate effectively with stakeholders at all levels.
Understanding of industry trends: Executives need to stay ahead of the curve and effectively incorporate AI technology into strategic planning, with a good understanding of industry trends and emerging technologies and how AI can impact such trends and technologies in the bioscience industry.
Overall, bioscience executives need to have a broad understanding of AI technologies and their applications in order to make informed decisions about implementing AI in their organizations. They should also be aware of the potential benefits and limitations of AI, and be able to balance these against the needs of their organization and the broader bioscience community.
Helpful Online Training sites:
- Designing and Implementing AI solutions in Healthcare.
- Artificial Intelligence in Healthcare.
- AI in Healthcare Specialization.
Author: David O. Chavez, DrBA
CEO and Co-Founder
Artificial Intelligence and Data Science Technical Recruiter
References:
Kumar, M., & Sarma, K. D. (2020). Machine learning for drug discovery in the pharmaceutical industry. In Artificial Intelligence in Drug Discovery (pp. 85-118). Academic Press. https://doi.org/10.1016/B978-0-12-818773-1.00005-5
Li, L., Liu, H., & Liang, Y. (2019). Application of machine learning in medical research. Current Pharmaceutical Design, 25(38), 4066-4073. https://doi.org/10.2174/1381612825666191206114233
Lin, Z., & Zhang, H. (2020). Big Data Analytics and Machine Learning in Bioinformatics: Recent Advances and Future Challenges. Journal of Medical Systems, 44(11), 196. https://doi.org/10.1007/s10916-020-01639-w
