As the world is grappling with unprecedented times owing to COVID -19 and a looming economic crisis across sectors, the life sciences sector is the one which is keeping afloat. But this is no reason to rejoice and times change, and so do industries and their fortunes. Thus, we must continue to adapt to survive.
Traditionally, life sciences as an industry have been a laggard to adopt Intelligent Automation. Still, with the looming health crisis, the need to innovate and rapid adoption of AI and Automation is being felt across functions like discovery, development, manufacturing and regulation, to bring medicines faster to the patients. Though AI holds a lot of promise, the industry has so far adopted a progressive approach with the initial wave of augmenting human capabilities by targeting time-consuming tasks, but this will gradually move to a more advanced decision-based intelligence-driven functions.
In this article, I will focus on the ever-evolving Automation ecosystem in the life sciences industry, drivers for adoption, market trends, challenges, future promise. I will also discuss use-cases where RPA, ML and AI solutions can enable improvement in operational metrics, cost savings, increased revenues and better customer experience. I will also list down some of the best courses I have come across in my journey to learn these skills and how you all can get started with it.
Intelligent Automation Market trends for Life Sciences
- The Life sciences Intelligent Automation market is expected to snowball (at a 75-85% CAGR to 2021 and 50-60% CAGR (Everest group, 2019). In 2019, Artificial Intelligence in Life Sciences Market was valued at USD 1092.44 million and by 2025, is expected to reach USD 3445.60 million, at a CAGR of 21.1% over the forecast period 2020-2025 (Mordor Intelligence, 2019)
- 94% of pharma professionals expect that intelligent technologies will have a noticeable impact on the pharmaceutical industry over the next two years (Pharma IQ – AI & Intelligent Automation Network, 2019)
- AI is poised to become “the primary drug discovery tool by 2027,” according to AstraZeneca’s Global Head of Enterprise.
- 94% of pharma professionals expect that intelligent technologies will have a noticeable impact on the pharmaceutical industry over the next two years.
India’s growth using AI in Life Sciences
India is the third-largest pharmaceutical market in Asia and is increasingly gaining government focus on expanding affordable health care. In the Union Budget FY19, the government announced the world’s most colossal National Health Protection Scheme, setting aside an investment worth USD 307.6 million. During COVID 19, India’s pharmaceutical industry has seen a rise of numerous health-tech startups as well as the emergence of AI healthcare horizontal in Tech giants.
Simultaneously, AI and machine learning are penetrating various industries across India, with health-tech as a significant beneficiary. According to a report by CIS India in 2018, AI could add USD 957 billion to the Indian economy by 2035. During coronavirus alone, about nine health-tech startups raised funding, and around 4800 health-tech startups are instrumental in public assistance.
Challenges for Life Sciences Industry –
- Increasing competition from generics – A lot of companies are facing the brunt of the market being flooded with generics as some of the long-lasting patents of Pharma companies have reached an end for their most-popular drugs. This is resulting in established market players to come up with new strategies on how to continue capturing the market share.
- Declining margins – With a lot of visibility on the ways of life sciences industries, there is a growing mistrust by the patients and people at large. The perceptions are changing with debates around the high margins of large players vis-à-vis the moral obligations.
- Growing regulatory pressure – Changes in areas such as privacy (GDPR) & cybersecurity (EU-MDR) have overburdened the sector. In contrast, the lack of clarity on political issues such as Brexit has added further confusion and uncertainty around guidelines.
- Rise in consumer expectations – In today’s world, the patients and consumers of any medical information are demanding transparency. Here Automation can be a handy tool to assess, analyze and help respond instantly using Chatbots or provide predictive/suggestive guidance to patients using Machine learning techniques.
Use-cases across Life Sciences Value chain –
- Drug discovery – This is an area which has come to a lot of limelight during COVID-19, and everyone is seeking answers where AI can help here. This is usually a long process which goes through multiple stages and may not always be successful. According to Deloitte, screening of small molecule libraries to identify new drug candidates, help cross-reference published scientific literature with alternative information sources, Drug optimization and repurposing among other areas.
- Clinical trials – For pre-clinical area, AI is used within the earlier stages of R&D; significantly to increase drug compound identification, DNA interpretation, to scan the drug safety data, to speed up a high-throughput screening or to manage genomic data with ML. For the clinical trial, ML can be applied to examine trial data, prediction of adverse events or medical conditions, improving predictiveness of diagnostic testing and enhancing clinical trial design with AI. Novartis claims that the deployment of QuantamBlack’s solutions has reduced patient enrolment times by 10-15%. Also, the company entered a partnership with IBM to make use of IBM’s AI platform, IBM-Watson, to improve clinical trial recruitment, and make use of intelligent AI algorithms to predict medication efficacy.
- Sales and Marketing – Under the ambit of Sales, the Automation solutions are being applied across areas like Sales planning (Segmentation and Targeting), Salesforce management, performance reporting, med-tech services etc. For Marketing, the Intelligent tools can be applied to functions such as Advertising and Promotions management by data mining data across platforms and analyze using NLP/AI, managing patient-oriented support program, Marketing research etc.
- Supply Chain and Distribution – This is another area which always struggles for accuracy and faster turn-around time for the customer’s orders. A range of intelligent applications such as Computer vision, Blockchain, IoT, Robotics Process Automation (RPA) & other AI/ML/Deep learning solutions can be leveraged in areas such as Packaging, Labeling, Validation, Order/Inventory Management, Equipment and device management, Order Tracking and Defect management.
Prospects for the Industry –
It is not surprising to say with high confidence that AI will be a crucial driver for the Life Sciences industry in the future to come. The world has been transformed and will continue to do so in Post-COVID-19 scenario, so needs to re-imagine and automate the way we do the activities today within Life sciences space. The need is to continue to adapt not just to survive the change but thrive.
As humans continue to evolve towards General AI, we would wish to create super-humans enabled by extensive AI-driven research in areas such as gene-therapy, DNA-regeneration, super-drugs to increase longevity and many more such advances. The Governments and Pharma Organizations are genuinely aware of the potential of leveraging AI-ML and are leaving no stone unturned to make this an economically profitable, socially viable and easily adaptable for the masses.
Getting started with learning AI and Machine Learning –
If you are wondering where to start and get going with learning AI-ML, below are some of the recommended courses. These are all MOOC suggestions, but you can also explore to enrol for long-term 1-year modular courses being offered by many Universities and Institutes. My opinion is before you do that it is good to explore some of the below ones to know if that matches with your future goals and interests as those would cost you much more.
- AI for Everyone – In this course, Andrew NG has come-up with a non-technical introductory course covering various dimensions of AI, their applications, identifying opportunities and ways on building strategy and team to drive Machine Learning and Data Science projects.
- Machine Learning A-Z: Hands-On Python & R In Data Science (Udemy) – Kirill Eremenko has devised this beautiful course and is for anyone who is starting from scratch. It builds upon your concepts step-by-step, so you learn all the nuances. Need not fret if you do not know R or Python, you will discover that during the course with guidance on any pre-requisites if required.
- Applied AI Certification by IBM – One of my favourite courses as it has a truly hands-on approach where you not only learn but also build and deploy AI applications leveraging freely accessible IBM-Watson platform. The Specialization consists of 6 courses and would take as many months to complete, but worth the effort spent on each course.
- AI-ML courses from Google – Google has come with a list of many courses as part of their Learn with Google AI platform and continues to add new ones regularly. They have an excellent repository of some exciting courses to cover ethical AI, Responsible AI practices to gain a broader perspective about the industry.
- Python for Data Science and Machine Learning Bootcamp – A very nominal priced course for the exhaustiveness of the topics covered. It has a detailed introduction to Python. Probably, the best path for python training and introduction to machine learning with Python.
Further, you can take Annual subscriptions to DataCamp, attend a Udacity Nanodegree program or even scan through so many courses on Kaggle. Also, I can add more to the above list, but I believe we as humans often get spoiled for choice when given many options. Thus, I have restricted my list, which should be enough to learn and apply the concepts. As you grow, you may continue to explore specific courses on topics which are apt for your area of work.
Lastly, I want to say: Learning is a continuous journey and AI-ML as a field is too vast to master in a few months. Be persistent and focused in your approach, and I am sure you will reach great heights in this field.
