Technology-driven innovations are dramatically altering the way modern life sciences operate, enabling a paradigm shift from treating diseases and their symptoms to preventing and curing them.
The UK-based Galleri trial uses Artificial Intelligence (AI) based algorithms to analyse blood samples. This enables the early detection of 50 types of cancers. Examples like this are many, and they make it increasingly clear that it is time for biopharma and medtech to transition. In other words, embrace modern technologies such as artificial intelligence, machine learning, and advanced statistical analyses to remain competitive.
We explore five tangible ways the life sciences industry can move toward technology-driven convergence.
Use AI and ML to develop new drugs and treatments
In early 2020, Exscientia’s AI-designed drug entered clinical trials for the first time. Since then, more than 160 AI-enabled drug discovery programmes have been under development. Fifteen of them have already reached the clinical development stage.
Such trials highlight the growing importance of in silico (an experiment conducted via computer simulation) drug development, which uses AI and machine learning (ML) models to discover new and effective medicines to treat conditions efficiently.
In the future, companies operating within life sciences can use these AI and ML technologies to identify structurally novel molecules and effectively develop new treatments and drugs.
Related: Better experiences and improved outcomes with AI
Leverage technology-enabled mental healthcare
A recent article by the Cambridge University Press describes just how promising digital CBT is. In that vein, companies operating in mental health can leverage gamification and digital simulation to deliver effective intervention programmes.
Digital therapeutics are becoming exceedingly popular in discovering new treatment paradigms in mental health, addictions, and neurological conditions. For instance, insomnia and other disorders have been successfully treated with the help of digitally administered cognitive behaviour therapy.
Adopt a customised approach toward treatments
Healthcare companies can also customise their treatments better with the help of genetic profile archetypes and genetic sequencing. Statistical models and predictive analysis can customise treatment approaches for everyone, enabling exact intervention techniques.
Life sciences companies can use these predictive and prescriptive technologies to make educated decisions regarding treatment options instead of relying on pharmaceutical treatments that have depended on extensive patient cohort studies.
Use AI-enabled decision making
Making treatment-related decisions and triaging can become more accessible with AI-enabled decision-making. AI algorithms can predict the best treatment outcomes and aid in clinical decision-making. Possible outcomes include:
- Detecting how responsive a tumour will be to a specific treatment.
- Pre-emptively identify potential infections in at-risk populations.
- Reduce the cost of treatment by choosing the most effective and affordable approach.
Related: Three reasons AI is not ready to replace radiologists
Focus on early detection and prevention
The COVID-19 pandemic has brought to light the importance of using technology to deliver telemedicine and remote monitoring of patients. By the end of June 2020, 41 per cent of adults had delayed medical care because of concerns related to COVID-19. The human cost could be staggering if one accounts for all the delays and treatment avoidance due to the pandemic over the last few years.
AI and ML-enabled devices can help with the monitoring and early detection of infections so that mortalities can be prevented by adequately monitoring patients remotely.
AI and ML will breathe new life into life sciences
Life sciences and medical industries have primarily depended on extensive cohort studies involving diverse patients. However, this approach has its limits.
Treatments cannot be customised, and practitioners cannot give adequate medical attention remotely. Thus, the convergence and adaptation of technologies such as AI, ML, and genomic sequencing are necessary for the future.
Such an adaptation will help companies discover new drugs, treatments, and therapeutic approaches and remain competitive. In addition, these advanced technologies will play a crucial role in the early detection and prevention of diseases rather than treating them at a later stage. In the future, we expect them to enhance the remote delivery of interventions as well.
This article appears in Omnia Health magazine. Read the full issue online today.
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