By Panna Sharma, President & CEO, Lantern Pharma, Inc. (LTRN)
The world of big data in the healthcare sector is vast and growing exponentially. Tools that leverage artificial intelligence (AI) and machine learning (ML) are critical to organizing, correlating, making sense of and generating useful discoveries from this avalanche of healthcare and medical data. Analysts suggest that 30% of the world’s data now comes from healthcare and is growing faster than any other industry with a CAGR (compound annual growth rate) of 36%. In genomics and biomarker driven drug development the growth rate of data is even higher, with analysts suggesting that it is doubling every 24 months.
To take full advantage of this data and develop powerful insights and correlations that can aid in the development of new medicines, help in the management of disease or reduce the costs associated with healthcare will require immense automation, massive near-time analytics and computational power that is only beginning to be developed. Those that can capture, control, analyze, and generate new insights from this data will be able to not only change the future of healthcare, but also develop new business models and paradigms that have only begun to be imagined.
These new healthcare-centric businesses have the potential to yield decades of future profits with vastly different operating models than the industry has experienced in the past. Like other industries where big data and AI have smashed the product development cycle and transformed choice and control for users—essentially creating entirely new paradigms for industry sectors—healthcare and medicine is now actively in the early phases of this transformation.
Transforming such a data-intensive, highly regulated and mission critical industry is not easy, and this is where the constant and focused application of computational techniques from artificial intelligence, machine learning and deep learning will be essential for progress to be achieved and for new companies to be birthed.
All areas of the healthcare sector will be affected directly or indirectly over the coming decade by AI. Two specific areas that are being actively transformed today are patient diagnosis and management, and drug development. Both sectors are being fundamentally transformed with positive benefits to the quality of patient care and towards the reduction of healthcare costs to society.
Patient diagnosis and management
AI offers the ability to correctly diagnose diseases at an earlier stage, predict disease and disease progression at a personal level and integrate care givers and clinicians earlier in the patient journey thereby improving patient care. Disease as diverse as diabetes to depression are being changed by the application of AI and data-driven business models.
Precision medicine, which is an integrative approach to patient management, applies emerging technologies such as AI, to analyze multidimensional datasets to discover and design personalized therapeutic treatments for an ever-growing list of diseases and simultaneously improve the diagnosis of a specific cancer or disease. Several areas of research and clinical practice are employing AI techniques to generate personalized treatments - the amount of data management and computational power required to do this at scale has only become recently available and even more recently economically feasible. In precision oncology, AI has been applied to various diagnostic and prognostic aspects of several cancer types, including breast cancer, blood cancers and non-small cell lung cancer.
In late 2020, the FDA gave approval to an innovative AI-based software (Genius AI Detection; Hologic, Inc.) that assists radiologists in detecting subtle potential cancers in breast images.
Conventional drug development is a lengthy process, with an average time from lab discovery to market of about 12 years. Fewer than 10% of drug candidates make it past the first phase of clinical trials. Using data-driven hypothesis testing—often from new biological data—to discover new drugs and revitalize abandoned or failed compounds can significantly shorten this timeline and reduce development costs.
In the case of historically costly and time-consuming clinical trials, AI and big data driven approaches to these challenges are poised to increase efficiencies, reduce costs, and bring life-saving medicines faster and in a more targeted manner to patients that can most benefit from that therapy.
Our ability to make an impact on healthcare—specifically on drug development—to benefit people and society will be enlarged and enhanced by AI in two ways. First, the timeline to bring new drugs to market will be greatly shortened resulting in lower development costs. The average cost of successfully bringing a drug to market was $2.6 billion in 2013. AI-designed drugs will eventually cut this cost dramatically both by reducing timelines and simultaneously reducing failure rates. Second, with reduced development costs and accelerated timelines for development, the ultimate cost to the patient and healthcare system will decrease as well while improving the benefit to patients globally.
We foresee a future in the coming years where dozens of new medicines (often for diseases that lack understanding or definition today) used in human clinical trials will be designed with AI. This will be the golden age of AI in medicine, resulting in an avalanche of new medicines along with revitalization of existing drugs that benefit society and help to fulfill the promise of personalized medicine.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.