We know AI has the potential to improve healthcare efficiency and this is widely discussed both in the media and within industry. While realistic expectations about the potential of AI solutions are required – such as the risks of misuse, and how it will be integrated – the ethical implications of slow progress or failing to adopt technologies must be deeply considered.
Indeed, there are moral consequences of not adopting AI, or of failing to use it to its full advantage – namely, missed opportunities for patients to feel better and for treatment to be improved, and for more people to access good care.
There’s a growing appetite for improving the accessibility and availability of this technology from both consumers and businesses, as well as healthcare professionals (HCPs). Increased investment and innovation in AI health solutions specifically show recognition of its potential to address unmet clinical needs, and practical applications for AI are already happening in pharma with proven results. In 2024, 40% of all digital health funding went to AI-driven start-ups, a rise from 33% the previous year.
Staying in analogue isn’t an option
In many cases of AI and integration, we’re beyond pilot stages. This is a crucial inflection point to accelerate and realise technology – go all in – and overcome any hesitancies to scale effectively.
True medical and scientific progress, especially within research and development (R&D), involves constant refinement and innovation, and a natural step is to adopt and test new technologies in these scenarios. R&D is well suited to utilise machine learning and data-driven approaches, where there are large data sets and complex patterns to discover. AI can expedite the development of life-saving treatments, cutting the development timelines by 50%, according to the Information Technology and Innovation Foundation, and can improve pharmaceutical R&D, leading to personalised medicine approaches and more efficient drug discovery.
There is emerging evidence that AI can outperform human clinicians in certain diagnostic areas such as breast cancer, where AI diagnostic tools have improved the accuracy of diagnosis by up to 13% by analysing subtle differences in medical scans. AI-driven personalised cancer therapies have led to a 40% improvement in treatment response and a 30% decrease in toxic side effects. The use of AI technology by HCPs is critical, paired with widespread training, which pharma has an important role in facilitating.
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