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AI has been gaining importance in the life sciences industry to enhance patient outcomes and increase operational efficiency and productivity.
FREMONT, CA: There is no more significant challenge for healthcare and life science companies than guaranteeing that their digital transformation, combined with better data management, can enhance patient outcomes, operational efficiency and productivity, and profitability goals.
The struggle to reduce expenses and enhance quality is one of the motivations of healthcare and life science's transformation from data-rich to data-driven. The emergence of at-risk contracting for providers, the danger of retail disrupting care delivery, and the influence of drug development on the difficulty of balancing speed to market with costs are just a few of the new drivers. Data is abundant in the health and life science sector.
For health and life science companies, the change of data into insights, and the establishment of a data-driven culture, provide value. Advanced analytics, such as Artificial Intelligence (AI) and Machine Learning (ML), which are getting traction in healthcare and life science and need enormous amounts of high-quality data, will be used to gather insights.
Since the COVID-19 pandemic, AI has taken on a new definition in the life sciences business. Digital resilience and sustainability have taken on a whole new meaning. Remote, decentralized, hybrid and virtual models were developed as the companies sought solutions to ensure clinical trial continuity.
People saw vaccine development times getting reduced and vaccinations on the market in less than a year. Artificial intelligence (AI) has rapidly become a part of everything.
AI have various uses in life science, for example, the vast usage of AI or ML in software in a medical device (SiMD) and software as a medical device (SaMD), increasing speed of patient recruitment, encouraging remote monitoring of sites and patients, the growth of digital biomarkers and digital therapies, pushing silico drug discovery, the development of digital twins, and prediction of the possibility of a patient with a severe event.
The ability of real-world data (RWD) to diagnose individuals and safety signals, construct synthetic control arms, and harness digital pathology and radiology data to support a precision medicine approach has lately become more significant. Valuable information can be gathered from EHRs and claims data that will help deep phenotyping and combining it with the genomic structure to generate a more profound knowledge of mechanisms of action and clinical results.
One may observe a significant convergence between pharma and healthcare as the two industries seek to benefit from one another. The utilization of AI and ML to generate scalable insights from this data is enabled by AI and machine learning, and federated learning models can fuel the secure usage of this data and learning models.