In recent years, with a noticeable acceleration in 2023, the artificial intelligence (AI) market has become increasingly driven by generative AI, a new technological frontier that uses machine learning and deep learning applications to generate new data, including images, music and text, that had not previously existed.
There are now many fields of application for this new technology, ranging from the ICT sector to the legal and professional services sector, and the health sector, with important impacts on the activities of companies and public administrations, as well as on people’s lives.
THE POTENTIAL OF GENERATIVE AI IN THE HEALTHCARE SECTOR
Specifically, according to health tech experts, generative AI is poised to affect research capabilities, clinical decision support, patient care, healthcare administration, communication, and clinician education and training.
For example, generative AI can assist throughout the drug discovery process, from helping design new compounds to selecting drug trial participants to optimizing searches through vast volumes of data. Therefore, according to the experts, generative AI’s use in research could reduce the cost and time required for breakthroughs by 20% to 30%.
As reported by the Boston Consulting Group, there are many use cases that testify to the potential of generative AI for health. NVIDIA is offering a set of generative AI cloud services that enable customization of AI foundation models to accelerate drug discovery and research in genomics, chemistry, biology and molecular dynamics. The services provide pretrained models and enable researchers to fine-tune generative AI applications on their own proprietary data. The offering has been adopted by drug discovery startups such as Evozyne and Insilico Medicine, as well as by incumbents such as Amgen.
In healthcare services, generative AI can be particularly useful in data analytics and software optimization. In fact, it could help medtech companies create more personalized and patient-centered devices incorporating software that allows for preventive maintenance and repairs.
The UK’s National Centre for Additive Manufacturing is applying generative AI to enhance the design of medical devices such as prosthetics and implants, tailoring them to the needs of individual patients. And the medtech company Implicity is using the technology to incorporate remote monitoring in pacemakers and implantable defibrillators.
In brain health, DiagnaMed recently announced the development of a platform leveraging generative AI to analyze electroencephalography signals in order to predict and monitor brain aging and provide insights and tools in the diagnosis, prevention, or mitigation of cognitive decline in patients with mental health and neurodegenerative disorders.
Generative AI can also aid clinicians in patient care and clinical decision-making. For example, it can offer enhanced medical image reading, assisted disease diagnosis and more personalized therapeutic plans.
Moreover, as reported in the recent white paper “Patient First Health with Generative AI: Reshaping the Care Experience” by the World Economic Forum, generative AI is proving to be a valuable tool in improving patient engagement in the care journey and changing how the patient can access health information, receive care and manage their health conditions.
Beyond the advantages in terms of diagnosis, personalized treatments, new drugs, nowadays, generative AI has come to the attention of many health systems worldwide, which are exploring its potential to solve current challenges. These include rising costs, physician burnout, workforce shortages, inflation and high interest rates to improve administrative efficiency and help make healthcare more accessible and sustainable.
According to a recent report by Deloitte, world leaders see promise in generative AI for improving efficiency (92%) and enabling quicker decision-making (65%). In addition, 75% of leading healthcare organizations are experimenting or planning to scale generative AI across the enterprise, while 82% have or plan to implement governance and oversight structures for generative AI.
HEALTH CARE CONSUMER SURVEY 2023: WHAT DO CONSUMERS THINK ABOUT GENERATIVE AI IN HEALTH?
Not only doctors and patients, but also consumers in general are particularly interested in the potential of generative AI to improve healthcare.
According to Deloitte’s 2023 Health Care Consumer Survey, which surveyed 2,014 U.S. adults, more than half of the respondents (53%) believe generative AI could improve access to healthcare, and 46% said it has the potential to make healthcare more sustainable. People who had had experience with generative AI were more optimistic with 69% thinking it could improve access, and 63% saying it had the potential to make healthcare more affordable.
Respondents also believe that generative AI is particularly trustworthy. Among those who have accessed generative AI for health and wellness, 19% say they have used it to learn about medical conditions, 16% to understand treatment options, and 15% to decipher the technical language. As well, the vast majority of these users (69%) rated the information as either very reliable or extremely reliable.
In general, consumers who have tried the technology for health and wellness appear to be the most optimistic about its potential. Slightly more than 70% of generative AI-users thought the technology could revolutionize care delivery, versus just 50% of consumers who have not used it.
GENERATIVE AI IN THE GLOBAL HEALTHCARE MARKET
Healthcare interest in generative AI is also confirmed by market data. A recent report by market.us reports that in 2022, the global Generative AI in Healthcare Market was valued at USD 0.8 billion and is expected to be valued at USD 17.2 billion in 2032. Between 2023 and 2032, this market is estimated to register the highest CAGR of 37%.
Dominating the market, with a revenue share of more than 65% in 2022, are mainly clinical applications (compared to system applications) used in various medical fields, including cardiovascular, dermatology, infectious diseases and oncology.
THE POTENTIAL RISKS OF GENERATIVE AI IN HEALTHCARE
Although the potential is great, generative AI in the healthcare sector, as well as in all other sectors, can give rise to several risks.
The main ones include:
- Biased Outputs. Generative AI results can reflect inherent biases in the underlying data. In response, generative AI companies need to assign experts to review the data and results and correct for bias through oversampling and other statistical techniques;
- False Results. Because the models are still evolving, they can sometimes generate results that are simply wrong (known as hallucinating in AI). Providers will need to make their models more transparent and emphasize the need for human review of outputs;
- Patient Privacy. Patient health data is sensitive and needs to be handled with extreme care. Companies with generative AI solutions should clarify data ownership with partners, strengthen cybersecurity, and look beyond existing data to the development of synthetic data.
Beyond the risks described above, an additional obstacle to the implementation of such technology are the excessive costs. While generative AI has the potential to reduce costs, experts have said, on the other hand, it requires significant resources, including time and money, to develop, train, optimize, manage and update models.
CONCLUSIONS
In conclusion, generative AI offers a powerful tool to address the many healthcare challenges and, in general, to increase efficiency, improve the quality of care, and create value for healthcare organizations.
However, the successful integration of these technologies in healthcare depends on the ability to balance the potential benefits and risks. Thus, first and foremost, it is essential, apart from a solid ethical framework, to invest in the skills and competencies of all the actors involved, so as to use technology in an informed and conscious manner and obtain information that is accurate, complete and reliable.