Artificial Intelligence (AI) is reshaping the healthcare industry, supporting drug discovery and medical trials, according to a QBE report calling for consolidated standards.
Compiled by Control Risks, the report points out that machine-learning technology is helping customise medical devices and treatments. This new era of patient centricity brings about many benefits, as well as emerging risks.
The four top areas where AI integration is likely to benefit life sciences are:
1. Drug discovery
AI has the potential to significantly reduce the time and cost required to bring new drugs to market. Historically, it took on average 10 to 15 years to develop a new drug and it often cost more than a billion dollars to bring it to market. However, algorithms can analyse libraries of molecular structures, predict the biological activity of potential drug candidates, and optimise their chemical properties for efficacy and safety. Researchers can thus prioritise lead compounds for further evaluation and expedite pre-clinical and clinical development stages.
2. Clinical trials
Looking at the number of drugs approved in the US per billion dollars spent in research and development over the past six decades, that number has halved every nine years. On average, out of seven drugs that enter the first phase of clinical trials, only one gets final approval. By leveraging analytics, researchers can enhance patient recruitment strategies and optimise trial protocols to ensure faster, more reliable results. Predictive modelling can also help identify biomarkers of treatment response, facilitating more informed decision making throughout the trial process.
3. Personalised medicine
Artificial Intelligence will enable personalised approaches tailored to patients’ unique genetic makeup, lifestyle factors and disease characteristics. AI will help analyse genomic datasets to identify biomarkers associated with disease susceptibility, prognosis, and treatment response. Healthcare providers will be able to optimise treatment strategies, minimise adverse effects, and improve patient outcomes. Associated with wearable sensors, this may enable personalised recommendations for diet, exercise or stress management, and form part of a prevention or wellbeing plan.
4. Customised devices
Combining AI algorithms with 3D printing, manufacturers can tailor implants, prosthetics and other medical devices to individual patient anatomies and needs. This improves comfort and performance, minimizing complication risks. The number of AI or machine learning-enabled devices keeps growing in the US.
These developments will come with various risks, the most obvious one being that hackers may seek to manipulate large-language models (LLMs) or access the datasets they rely on. In addition, algorithmic bias might negatively impact personalised medicine, and customised devices might malfunction, leading to inaccurate diagnoses or incorrect treatments.
AI integration is bringing new players into the healthcare industry and might lead to a culture clash between the risk-taking ethos of technology innovators and the cautious approach of medical professionals. It could also create challenges for regulators which tend to respond slowly and inconsistently across markets. There is a strong need for consensus on best practice and consolidated standards. Last October, the World Health Organization (WHO) published guidelines to regulate AI effectively.
An ‘AI rush’, whereby organisations race to integrate AI into their operations, might distract resources and compromise protocols. What is more, additional or updated skills will be required for drug development and administration, and the provision of healthcare. Human errors will persist AI contexts, but they will likely be linked to overreliance on technology or improper understanding of patterns.
Alex Bell, Senior Underwriter at QBE, comments: “With consolidated standards and effective regulation, the life sciences industry will be able to harness the potential of Artificial Intelligence. Far from stifling innovation, this clearer environment will help grasp the opportunities that machine learning can offer for human health.”
The full report compiled by Control Risks is available on the QBE website: Life sciences meets AI: a promising future for patient centricity.
