ganesh nikam
Unlike all previous waves of IT and digital innovation, where different industries have gained the immense benefits of technology at varying paces, the adoption of AI is a whole different story. Nearly every industry that was previously considered a late adopter showed up very early on the starting line and prepared at full throttle. The Indian biopharmaceutical sector is one of such early starters and AI is being discussed and strategized from board to boiler him level. We all know that the rate of change in AI progress is so fast that we cannot afford to miss or delay any part of it.
All the significant companies in the Indian biopharmaceutical sector have been able to start embedding AI quite early on. This time around, his IT and digital teams within the company are well established and mature enough to make sense out of the box, and senior management sees information technology as a key component of their overall strategy. Priority areas where AI and machine learning are being used and prioritized are drug discovery, manufacturing optimization, clinical trials, precision medicine and pharmacovigilance.
Drug discovery: AI can be used to analyze large datasets of biological and chemical information to identify potential drug candidates, predict their efficacy, and optimize molecular architecture.
Manufacturing optimization: AI can be used to optimize manufacturing processes, monitor quality control, and reduce waste in pharmaceutical manufacturing.
Clinical trials: Use AI to identify patient cohorts, predict response rates, and optimize treatment protocols to design more efficient and effective clinical trials.
Precision medicine: AI can be used to identify subgroups of patients who are more likely to respond to a particular treatment based on genomic or other biomarker data.
Pharmacovigilance: AI can be used to monitor drug side effects and detect potential safety issues early in the drug development process.
Due to the presence of biopharmaceutical companies across the value chain, the adoption of AI and ML is fairly pervasive.
There are two overall effects on jobs and responsibilities. One is to bring in pure data science, AI/ML resources as part of core teams in each relevant department to help strategize and implement AI/ML interventions. The second is the retraining of existing middle management resources on this new technology, which is part of the data-driven decision-making process and culture change. So, in fact, there has been a significant increase in the employment of pure IT employees in the biopharmaceutical industry, where data knowledge or certifications are preferred in existing key biopharmaceutical resource resumes. increase. I wouldn’t be surprised to hear that within a few quarters, every major biopharmaceutical company is hiring his CTO and elevating him to the top of their decision-making body.
Biopharmaceutical companies are also beginning to work with universities to incorporate data science, AI, and ML into curricula in all relevant areas, and to provide in-house and paid training to existing resources. It also requires significant investment in AI tools and software and integrating them into the organization’s IT bloodline.
The future of this industry over the next decade will be determined by the ever-increasing predictive and computational power of AI and ML algorithms, which will also fuel debate about the ethical and moral aspects of the medical-human conflict. .
the author is Biojobz CEO
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