Generative AI is already entering multiple health care segments, and the new technology has the potential to improve care quality and reduce inequities in care delivery and research. But an analysis from the Boston Consulting Group warns that while generative AI algorithms have the capacity to dramatically transform the health care industry and all its ecosystems, executives must avoid a number of potential risks before broadly implementing generative AI. Learn how organizations and companies are already leveraging the technology and expand your perspective of what the emerging future of AI-powered health care might hold.
- Generative AI is dramatically impacting major health care segments.
- Before adopting new generative AI technologies, prepare for the risks.
- Embrace strategic change to integrate generative AI into your health care ecosystem.
Generative AI is dramatically impacting major health care segments.
As generative AI enters health care, executives must both leverage the technology’s potential, while avoiding potential risks. Several health care providers are already harnessing generative AI solutions to assist in diagnosis and patient care and monitoring. For example, some are using products from the digital pathology company Paige.AI to improve the efficiency and accuracy of prostate cancer diagnoses. In the future, generative AI solutions could become more personalized and preventative, prompting suggestions for patient behavior changes and interventions before conditions worsen. Public health agencies, government ministries and other organizations could also use generative AI to better anticipate public health needs and execute programs. For example, BioNTech’s acquisition of InstaDeep enables it to create an AI-powered early-detection system for new variants of the COVID-19 virus. Future generative AI tools could help public health organizations, such as Doctors Without Borders, anticipate outbreaks and better allocate resources.
“Generative AI is accelerating drug discovery, improving clinical-trial planning and execution, and leading to more precision medicine therapies.”
Payers are beginning to utilize generative AI to lower costs and manage high-risk patient risk segments, gleaning data insights from an individual’s medical history, as well as social and demographic determinants of health. In the pharmaceutical industry, companies are capitalizing on generative AI’s potential to accelerate drug discovery and the planning and execution of clinical trials. For example, Insilico Medicine completed its preclinical phase of its idiopathic pulmonary fibrosis drug faster than average, in just 30 months. Generative AI is disrupting medtech as well, with innovators creating and optimizing patient-centered devices. For instance, Implicity is incorporating remote monitoring in implantable defibrillators and pacemakers, while DiagnaMed is creating devices that monitor and predict brain aging via electroencephalography signals, mitigating cognitive decline.
Before adopting new generative AI technologies, prepare for the risks.
Providers must contend with the following inherent risks before broadly adopting generative AI:
- Bias – Generative AI’s underlying data can reflect pervasive inherent biases.
- Errors – Evolving generative AI models can generate inaccurate results. Providers must embrace transparency and human review processes.
- Privacy – Generative AI companies and partners must carefully clarify data use and ownership, due to the sensitivity of patient medical data.
- Opaque results – Generative AI functions much like a “black box,” which health care users may find unsettling. Health care organizations must be prepared to explain how algorithms work and how data sets trigger prognoses.
- Overreliance or misuse – To ensure patients don’t rely too much on generative AI insights, hospitals, payers and clinicians must clarify that AI insights are recommendations, not mandates, while creating clear guidelines for how to act on these insights.
Embrace strategic change to integrate generative AI into your health care ecosystem.
To adopt and implement generative AI, health care workers must build the right foundation. Create your generative AI strategy, exploring new business models and possibilities. Next, build your data systems, investing in data analysis and management tools. Recruit generative AI experts, positioning in-house team members to spot new opportunities and train decision-makers and stakeholders in leveraging the new technology.
“Generative AI involves uncertainties and risks, but it also holds the potential to dramatically increase efficiency, improve the quality of care, and create value for health care organizations.”
Expand your network, building strategic partnerships, partnering with consulting firms and tech industry players, ensuring you don’t miss opportunities. Finally, make sure you integrate generative AI into your broader ecosystem by establishing data interoperability and the appropriate systems to support the new technology. Collaborate with regulatory bodies, co-developing safe and effective generative AI health care solutions. You can’t afford to ignore generative AI’s myriad new value creation possibilities, but make sure you carefully plot your path forward, eliminating risks, before attempting to capitalize on the rapidly evolving technology’s vast potential.
About the Authors
Matthew Huddle, Josh Kellar, Krishna Srikumar, Krishna Deepak and Daniel Martines are professionals with the Boston Consulting Group.