Applications of AI in Life Sciences and Health Care
To date, most organizations in life sciences and health care (LSHC) have only scratched the surface of AI’s potential — primarily using it to automate repetitive tasks and standard business processes. However, AI is now widely recognized as a strategic business issue in this area and is actively being discussed at the board and C-suite levels.
By combining AI technology with the fields of medicine and science, organizations are looking for opportunities to transform some of their most critical processes and achieve sustainable competitive advantage through AI.
The application of AI has the potential to expedite drug development — helping researchers to identify and validate genetic targets, and to design novel compounds. It also has the potential to help companies launch and market products more effectively, and to make supply chains smarter and more responsive.
According to a recent Deloitte survey about the use of AI in life sciences globally, the top outcomes that life sciences companies are trying to achieve with AI are:
of respondents in a recent survey revealed that they expect AI and machine learning to improve treatment recommendations for individuals.
of global health care companies will implement artificial intelligence strategies by 2025.
Facing the top obstacles
Though the AI health market is growing rapidly, implementing technology for medical purposes still entails a range of challenges. At a high level, the key to successful AI adoption requires people, processes, and technology to work in harmony. Ensuring patients’ trust, upskilling talent, having a clearly defined digital strategy, along with ways to measure ROI, are among top concerns that hinder successful AI adoption in the health care sector.
For life sciences and health care organizations, AI offers tantalizing prospects for swifter, more accurate clinical decision making and amplified R&D capabilities. However, open issues around regulation and clinical relevance remain, causing both technology developers and potential investors to grapple with how to overcome today’s barriers to adoption, compliance, and implementation.
Four main challenges barriers to AI adoption:
- Integrating data from various sources into a proper data infrastructure
- Identifying use cases with the highest potential value
- Lack of adequately qualified workers with the right technical skill sets to support AI innovation
- Anxiety over the change AI can/will bring to the industry
Discover new applications and benefits for AI
AI is already proving its value in making processes more efficient, and over the next three to five years, AI is expected to have a transformational impact on biopharma research and development (R&D), particularly for drug discovery.
Meanwhile, life sciences companies will likely continue to conduct AI pilots and proofs-of-concept in many other parts of the value chain.
In health care, AI adoption is still largely in its infancy. However, it is quickly gaining traction — and ultimately Al is expected to have a huge transformational impact on the business of health care — and on how health care is delivered. Today, most early use cases for AI in health care focus on administrative tasks and basic automation, rather than more sophisticated clinical applications such as disease diagnosis and care delivery, which seem riskier and require higher levels of intelligence. However, more advanced AI applications are already emerging that demonstrate the practical viability of sophisticated clinical use cases (e.g., the use of AI for imaging diagnoses).
For most organizations, the single most important AI building block is data: getting access to the rich data that AI systems require, and then managing that data in a coordinated way across the enterprise. With robust data, the potential use cases for AI in life sciences and health care are nearly limitless.
Understanding what can be achieved by AI today
As AI becomes a standard business tool — and competitive necessity — organizations in life sciences and health care will need a clear vision and strategy for harnessing the power of AI. They will also need the building blocks in place to develop and deploy AI solutions at scale. These building blocks include: the right IT infrastructure; the right talent and skill sets; and alliances/ecosystems that enable them to develop or access the AI capabilities they need.
Explore ten use cases depicting how life sciences and health care organizations are harnessing the power of AI to improve process efficiency.
Trials with less error
Digital Data Flow for Clinical Trials
Use cognitive automation to integrate trial data from multiple systems, populate standardized digital data elements, and generate trial artifacts such as case report forms and study reports.
Smarter drug manufacturing
Drug Manufacturing Intelligence
Use algorithmic models and sensor data to maximize factory yield and productivity by predicting manufacturing deviations and proactively suggesting corrective actions.
Drug Marketing Omnichannel Engagement
Use machine learning models to predict the best ways to engage with patients and health care professionals (HCPs)—and to optimize marketing spend across media channels.
Voice of the Patient Insight
Use AI to analyze patients’ and HCPs’ social media feedback, complaints, and adverse events — generating insights that can improve product design, packaging, and educational materials.
Compliance amidst complexity
Proactive Risk and Compliance
Use AI to automate the analysis and aggregation of data when identifying risk and compliance issues—recommending the next best action and possible mitigation methods.
Use AI to improve every aspect of patient engagement, from scheduling appointments and accessing medical records, to communicating with health care staff and care coordination teams.
Next-level claims handling
Health Care Revenue Cycle Optimization and Efficiency
Use AI to automate claims submission and payment for pre-care, day-of-care, and post-care activities.
Computer Assisted Diagnosis
Use AI technologies to diagnose medical conditions more efficiently and accurately.
Medicine that is truly personal
Precision Medicine & Personalized Health
Use predictive insights to proactively diagnose, prevent, and treat a future illness based on an individual’s lifestyle, real-world environment, biometric data, and genomics
Use predictive AI to forecast peaks and valleys in patient volume and then adjust hospital staffing and resource levels accordingly.
Navigating the future of AI for life sciences & health care
Over the next several years, the focus for AI in life sciences and health care should be on accelerating drug discovery – and drug development – while also centering on personalizing every aspect of the patient experience. As AI becomes a standard business tool and a competitive necessity, life sciences companies will need a clear vision and strategy for harnessing the power of it. They will also need the building blocks in place to develop and deploy AI solutions at scale. AI continues to permeate the health care industry as health care providers and their focus on patient experience create significant value for patients and providers alike while setting the stage for longer-term use of AI in the most sophisticated clinical applications.
Explore our ten emerging AI use cases in the LSHC industry to uncover future-driven opportunities:
Get in touch