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