AI in Life Sciences: Powering Tomorrow's Medical Discoveries

AI is no longer just the work of science fiction. It is here and delivering results in both everyday life and in business. It is revolutionising the way that we work and play, and it could be the answer to the medical discoveries of the future. But is it without risk? In this post, we explore the possibilities that AI offers to the life sciences industry and consider what challenges may need to be overcome in order to exploit the technology as safely and effectively as possible.

Benefits already derived

1. Research and development. Machine learning, one of the earliest elements of AI technology to be readily embraced by biotech firms, has revolutionised the way in which data is collected and analysed. By processing past data, the technology is able to form very accurate predictions about potential future outcomes, collate and tag data according to specified parameters or codes and support scientists in developing new methodologies across a range of sectors.

2. AI-developed medicine. Following three years of innovation and discovery, Insilico Medicine have announced that their AI-developed drug for treating idiopathic pulmonary fibrosis has reached Phase 2 patient trials. This is the first entirely AI-developed drug that has been approved for human trials and heralds a new dawn in which AI technology can not only support scientists but work alongside clinicians to create treatments for human conditions.

3. Medical diagnosis. The application of AI in assessing medical images has reduced physician workloads, errors and diagnostic timescales by making accurate evaluations of the disease from which the patient is suffering and developing appropriate treatment plans which improve decision making and enable more timely treatments to be offered.

Future uses for AI technology

1. Personalised medicine. Each human has their own genetic structures which make them unique, and whilst this often delivers benefits such as a natural resistance to particular diseases, it also opens the possibility for particular individuals to be afflicted by rare diseases for which no current treatment exists. AI technology allows operators to analyse the individual's genetic makeup, medical history and lifestyle factors in order to develop a tailored treatment plan, improving patient outcomes and reducing the risk of adverse reactions.

2. Genomics research. AI's success in research and development means that data can be analysed more quickly, leading to faster drug discoveries, biomarker discoveries and clinical trial optimisation. All of this means that genomics research can proceed more quickly than previously possible, ultimately improving patient outcomes.

3. Reduced funding requirement. Particularly of benefit to early stage European and US biotech companies and startup organisations, AI can drastically reduce the cost of developing new drugs, making it possible for them to compete against larger, established organisations when seeking treatments for specific or rare conditions.

4. Identifying potential trial participants. AI allows clinical trials to scaleup at speed by identifying suitable candidates based on defined criteria set by the trial organiser.

5. Optimised marketing. Pharmaceutical companies can draw on trial data, analytics and real world evidence and use this to market their product extremely effectively to a far wider audience than has previously been possible, using AI-powered algorithms to target their communications to the specific audiences that are likely to purchase their product.

6. Improved safety. AI technology could potentially increase the safety of new drugs by analysing huge tranches of data and trial results in order to identify potential issues prior to product launch, improving the reputation of biotech firms and reducing the risk to venture capital investors.

Challenges of AI technology

1. Data protection and cyber security. AI tools can access significant quantities of data and store it to improve their capabilities. Whilst this offers the ability to perform research and development very efficiently, it introduces data protection and cyber security concerns. Should the data accessed or generated by the AI tools be breached, it could have significant ethical, regulatory, financial, reputational and legal repercussions for the firm operating the technology.

2. Unfair bias towards larger firms. Whilst the hope is that AI technology will level the playing field between small and large biotech firms, the risk remains that those larger organisations with greater levels of funding will be able to exploit the technology more quickly and effectively than their smaller counterparts, thereby extending the capability gap between them.

3. Changing workforce requirements. AI has the potential to dramatically alter the life sciences industry, by changing the skill sets that are required and requiring that personnel integrate and collaborate with the technology. Firms must accept that the introduction of such technology will require that their workforce is continuously upskilled to handle the changes that are imposed upon them in order that they can effectively exploit the technology and remain relevant.

In conclusion, AI technology is already delivering improvements across the life sciences industry and its ability to analyse vast quantities of data offers the potential to accelerate innovation and the way in which the industry approaches medicine in the future. There are many challenges and ethical considerations that must be overcome in order to use this technology as effectively as possible, but since it offers immense promise of a safer and healthier tomorrow, biotech firms will continue to investigate its possibilities.

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