AI and Life Sciences: Navigating Risks and Challenges
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AI and Life Sciences: Navigating Risks and Challenges

By Rajarshi September 21, 2023 - 87 views

With the increasing penetration of artificial intelligence (AI) in life sciences, there has been a barrage of questions regarding the risks and challenges involved in this integration. While AI has already started its transformative journey throughout multiple other industries, the life sciences sector has recently woken up to the potential of the same. 

Some factors that are key moot points in this regard include the role played by AI in developing COVID-19 vaccines in quicker time (less than one year as opposed to a decade in most cases), AI-driven drug discovery where a novel drug candidate was found for liver cancer in only 30 days, and more. Even Google Cloud has unveiled new AI-backed tools that facilitate quicker drug discovery. Many other technology companies are coming up with tools for automating processes that were manual and time-consuming in nature earlier. 

How it stacks up 

Life sciences and healthcare AI have already reached a watershed point where there are challenges and disruptions to contend with, but the speed and scale of adoption continue unhindered. Here are some points worth noting in this regard: 

  • An article published in Nature Reviews Drug Discovery in 2012 talked about Eroom’s Law or Moore’s Law in the reverse manner, indicating the decline in R&D efficiencies in pharmaceutical sectors. 
  • The study highlighted how R&D costs for new USFDA-approved drugs had gone up considerably for six decades. 
  • Yet, the journal started noting changing trends by 2020 in the earlier decade. This turnaround was majorly attributed towards better decision-making information, particularly for disease biology or modulating the same. 
  • This led to newer mechanisms for treatment and also for managing diseases which do not have effective treatments or therapies.

Yet, ethics, data privacy, regulatory aspects, and other challenges must be tackled with care to ensure widespread benefits from integrating artificial intelligence (AI) in life sciences. Let us first look at the range of its applications in this space. 

Applications of AI in life sciences and healthcare

Here are a few points that should be noted in this context: 

  • AI evolves in sync with every drug development stage. This includes genomic data analysis, discovery, diagnostics, clinical trial design, and biomarker development. 
  • With each improvement, timelines come down and success rates go up accordingly, enabling more patients to get better treatments. 
  • AI is speeding up drug development which usually required 10-15 years earlier with costs of a whopping $2 billion and more. The success rate was also capped at less than 10% earlier. 
  • AI is also positively impacting clinical trial design, eliminating the need for manual and repetitive collection of data which may take up to 7 years in most cases. It is enabling quicker patient selection for trials while lowering the overall time and work involved in the process. 
  • AI is also helping analyze vast medical data amounts while enabling the generation of insights for identifying ailments and creating personalized plans for treatment. 
  • AI and ML (machine learning) are also working to analyze images while identifying features that may not otherwise be identified by human beings. This has immense potential for the swift detection of heart ailments, lung and prostate cancer, diabetic retinopathy, and several other diseases. 
  • AI is also helping diagnose ophthalmic ailments quickly, which is positively impacting the ophthalmology sector. 

Now that the benefits of AI are clearly visible, let us take a closer look at the challenges mentioned above and the ways to navigate them for swifter progress in the domain. 

Major challenges of AI in life sciences

Here are the risks that still remain while deploying artificial intelligence (AI) in life sciences. 

  • Confidentiality of Patients – Data privacy in life sciences is a key concern, particularly when it comes to patient confidentiality. The usage of patient health data is vital for any innovation to succeed. However, since it depends on data gathered from patients, securing such sensitive information is a must. This requires a calibrated approach towards safeguarding patient confidentiality and making sure that the transmission and storage of the information are done securely. Anonymity sometimes goes for a toss when big datasets are used. Data security and governance protocols should thus be in place along with compliance measures for enabling better processing, collection, and storage of patient information. This will help companies keep data breaches at bay while securing patient data more effectively. 
  • Data Bias and Quality – Ethical AI models are the need of the hour, especially since the life sciences sector depends on massive data sets from patients and healthcare providers. They have to make sure that these are reliable for churning out suitable insights. This may be challenging, with AI models being trained on the basis of real-world information that may otherwise have biases. These may get embedded in algorithms, leading to unwarranted consequences in some cases. For getting suitable value from insights, life sciences companies should create dataset designs with bias mitigation strategies in place. Following the best practices in this space is important, including those published by resources like Google AI and others. 
  • Regulatory Adherence – Another hurdle for artificial intelligence (AI) in life sciences is regulatory compliance. These requirements on ethical practices and the safety of patients are often more stringent for the life sciences sector. Whenever these are not met, there are higher risks of penalties, legal action, damage to organizational reputation, and so on. Companies should stay in the loop on regulations and set up compliance programs with frequent audits and proactive steps. 
  • Other Challenges – One of the biggest hurdles towards the advancement of AI-driven initiatives is the absence of skilled professionals. Companies can steadily combat the shortage by tying up across the academic and industry spectrum for knowledge and data-sharing. 

Signing off, it can be said that the AI-enabled transformation drive is now in the second phase, i.e. completing patterns and going beyond the initial brief of recognizing them. The life sciences sector will greatly benefit from this current AI stage, provided it can counter the challenges mentioned above. 


  1. What is the role of AI in the life sciences industry, and why is it important?

AI has a vital role to play in the life sciences industry, enabling faster drug discovery and development along with boosting clinical trial design and data-gathering. It helps analyze vast data sets and generate better insights from the same. 

2. What are the key challenges and risks associated with implementing AI in healthcare and life sciences?

There are a few challenges and risks that companies have to face while implementing AI in the life sciences and healthcare industry. These include the lack of skilled talent, regulatory compliance hurdles, ensuring data privacy and patient confidentiality, and steering clear of biases in AI algorithms.

3. How can data privacy and security concerns be effectively addressed when using AI in life sciences?

Data security and privacy concerns can be tackled effectively with a few proactive steps while using AI in the life sciences sector. These include dedicated patient confidentiality and privacy approaches along with an increased focus on secure data transmission and usage. Governance and data security protocols should be established as per regulatory standards for secure storage, processing, and collection of patient data. 

4. What ethical considerations should be taken into account when deploying AI in medical decision-making?

The biggest ethical consideration that should be kept in mind while AI is being used for medical decision-making, is the elimination of biases. While training AI models based on real-world data and inputs, there are unconscious biases that get embedded into the same. This may have negative consequences for patients if they are not tackled at the outset.

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