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News • Concerns about hurried adoption

Survey: Is medical AI taking over too quickly?

New research reveals the majority of the UK population (57%) is worried artificial intelligence (AI) is being adopted too quickly into personalised medicine to allow for the necessary training to be able to use these systems safely.

Business insurer QBE surveyed 2,000 members of the UK public on their attitudes towards AI and personalised medicine - individual approaches tailored to patients' unique genetic makeup, lifestyle factors and disease characteristics. Personalised medicine is a new and evolving component of the life sciences industry - one of the most dominant economic sectors in the world. It is estimated that in 2023, one in every 121 employed people in the UK worked in the life sciences sector, contributing more than £13 billion to the economy.¹

The integration of AI into life sciences is extensive. Half of the world’s 50 largest pharmaceutical companies have entered into partnership or licensing agreements with AI companies. AI in life sciences is projected to reach a market worth of $7.09 billion by 2028, growing at a compound annual growth rate (CAGR) of 25.23%.² The UK AI market is worth more than £16.8 billion and is expected to grow to £801.6 billion by 2035.³ Reports state the UK is the third globally in AI (behind only the US and China) with recommendations for it to be adopted in life sciences and other sectors⁴ as a key economic driver.

Strong data foundations and governance will be critical to prevent vulnerabilities as many companies move to operationalize AI across their enterprises
Paul Hudson

QBE’s research shows more than a third (36%) are unaware of AI's role in personalised medicine, and nearly two thirds (60%) are not informed about the swift adoption and integration of AI. However, among those who are informed, the majority (54%) believe that AI in personalised medicine will improve healthcare treatment.

Polling before the General Election details how healthcare and the NHS are the second most important issue for all voters across the UK, with most voters indicating that it was their top priority just behind the overall economy.

Multinational pharmaceutical company Sanofi has collaborated with biotech Exscientia to explore new treatments for cancer and diseases linked to the immune system. Using Exscientia’s AI-based capabilities and personalised medicine platform, Sanofi’s scientists can test drug candidates against actual human tissue models, years before a clinical trial.⁵ Sanofi has also partnered with pharmaceutical companies Insilico Medicine and Atomwise to speed up medicine development using their AI-driven platforms.

Article • Technology overview

Artificial intelligence (AI) in healthcare

With the help of artificial intelligence, computers are to simulate human thought processes. Machine learning is intended to support almost all medical specialties. But what is going on inside an AI algorithm, what are its decisions based on? Can you even entrust a medical diagnosis to a machine? Clarifying these questions remains a central aspect of AI research and development.

Sanofi CEO Paul Hudson says: “It’s clear that we stand at the crossroads of a great expansion in medical discoveries, but to take full advantage of AI, there are several challenges that will greatly impact the pharma industry’s ability to unlock potential. Regional differences in regulation will guide restrictions on where AI can be employed, standards, and what constitutes high-risk applications. Concerns about data quality, security, privacy, and trustworthiness have all threatened to slow the uptake of AI. Alliances and organizations are emerging to help companies self-regulate. Strong data foundations and governance will be critical to prevent vulnerabilities as many companies move to operationalize AI across their enterprises.”⁶

While AI holds great promise for enhancing personalised medicine and the life sciences industry, we must proceed with caution. Continuous review of AI systems is vital to identify and rectify any glitches that could jeopardise patient safety
Tim Galloway

Vivan Therapeutics uses AI for personalised cancer treatments. Its TuMatch software can match a colorectal cancer patient’s unique tumour profile with an appropriate drug therapy. It replicates a patient’s genetic profile and can screen thousands of drug combinations for a treatment that’s tailored to them.

Vivan Therapeutics CEO Laura Towart says: “AI has the capacity to significantly enhance the precision and effectiveness of personalised medicine. By analysing vast amounts of data, AI can help tailor treatments to individual patients, improve outcomes and reduce side effects. It is essential that we educate the public about the benefits and safeguards associated with AI in personalised medicine. Our goal is to build trust by demonstrating the value and safety of AI technologies.”

QBE Europe’s Portfolio Manager Life Sciences Tim Galloway says: “While AI holds great promise for enhancing personalised medicine and the life sciences industry, we must proceed with caution. Continuous review of AI systems is vital to identify and rectify any glitches that could jeopardise patient safety. Substantial training programs are necessary to ensure that healthcare professionals are proficient in using AI technologies correctly. AI will likely become a key aspect of risk management in life sciences, particularly around patient centricity. By advocating for stringent risk mitigation strategies, patients will be better protected and help AI technologies deliver without unintended consequences. Navigating these uncharted waters will be challenging, requiring life sciences companies and professionals to embrace innovation while maintaining ethical, safety, and regulatory standards.”

Likely benefits of AI integration in life sciences include:

Drug discovery and development
Genomics and personalised medicine
Medical device customisation
Imaging and diagnostics
Clinical trials
Drug repurposing
Regulatory compliance
Biotechnology
Healthcare management

Areas of risk of the rapid adoption of AI in life sciences include:

No consolidated standards yet
Training gaps
Human risk
Data privacy and security
Medical device failure

Key findings from the research include:

57% of respondents are concerned about AI systems being adopted into personalised medicine too quickly to allow for the necessary training to be able to properly use these systems safely, 17% are not concerned
57% of respondents are worried that AI is being integrated too rapidly for necessary training and safe use
49% of respondents are concerned by the introduction of AI into personalised medicine when it comes to protecting their personal health data, 23% are not concerned
At the point of polling, 34% respondents had heard or seen anything about the rapid adoption and integration of Artificial Intelligence (AI) into personalised medicine, 60% had not
35% of respondents believe AI in personalised medicine is currently improving healthcare treatment, 11% believe it worsens it and 36% are unaware of its use. Of the people that are informed, 54% think AI in personalised medicine will improve healthcare treatment.

References:

Source: QBE European Operations

26.07.2024