- The author of this article is Dr Anil Heroor, Head-Surgical Oncology, Fortis Hospital, Mulund ~
When the Coronavirus pandemic took us by the storm, Cancer patients suffered the most as oncology screening and treatment paused for a while. Having said that, when hospitals switched to digital mediums to connect with their patients, hope began to blossom again. The interrupted service system taught us many lessons, some of which will change the cancer care landscape forever. One such lesson was the need to integrate Artificial intelligence (AI) applications in Cancer screening and treatment.
THE ROLE OF AI IN CANCER SCREENING AND TREATMENT: AI approaches have the potential to affect several facets of Cancer care, making it more accessible and accurate. A recent Niti Aayog policy paper on the National Strategy for Artificial intelligence (AI) compounds this thought. The report states that Cancer screening and treatment is an area where AI provides tremendous scope for targeted large-scale interventions. The report further reveals that India sees an incidence of more than 1 million new cases of Cancer every year. Early detection and management are instrumental in building a robust Cancer care system in the country. It also highlights that good quality pathology service is the essential building block of Cancer care, which unfortunately is not easily available outside select Indian cities. For an annual incidence of more than 1 million new Cancer diagnoses every year, India has barely 2,000 pathologists experienced in oncology, and less than 500 pathologists who could be considered as expert onco-pathologists. Therefore, AI and machine learning solutions can plug this gap and is a pre-requisite in the successful implementation of any large-scale Cancer intervention. Just as Niti Aayog and the government finds an immense scope in AI to improve Cancer care in India, many healthcare organizations and oncologists are already exploring this opportunity.
IMPROVING ACCURACY AND SPEED: Integration of AI technology in Cancer care could improve the accuracy and speed of diagnosis, aid clinical decision-making, and lead to better medical outcomes. AI-guided clinical care has the potential to play an important role in reducing health disparities, particularly in areas where Cancer services are meager.
IMPROVING SURGICAL OUTCOMES: In an operating theatre (OT), surgeons must always be precise while performing surgical tasks. As repetitive tasks can be challenging, using integrated or collaborative techniques of robots and AI applications can be extremely beneficial in achieving a higher level of precision and better medical outcomes. AI-guided surgical robots can help the surgeon navigate and control the trajectory, depth, and speed of their movements with great precision. They are especially well-suited for complex Cancer procedures. However, there is a need for deep learning on surgery to achieve great advancements in medical outcomes. So, we can hope to see a similar kind of transformation that we see in the aeronautics industry today. For example, the fly-by-wire technology that replaced conventional manual flight controls of an aircraft with an electronic interface. Having said that, for AI in healthcare this might take a very long time as AI needs to become more widely applicable and acceptable.
IMPROVING SURGICAL LEARNING: Simulation-based training is increasingly being used for assessment and training of psychomotor skills involved in surgical medicine. The application of AI and machine learning technologies has enabled new methodologies to utilize large amounts of data for educational purposes. This can be a turning point for surgical students and oncologists to improve surgical capabilities.
IMPROVING CANCER RESEARCH: Data collection and mining in cancer gives another scope for the use of AI and deep learning mechanisms. Machine learning and AI can enable large-scale Cancer research that can improve Cancer detection, surveillance, and medical prognosis.
AI as we all know is a great value-add to medical professionals. It is an enabler and an assistive guide to doctors to efficiently provide clinical care and achieve better patient outcomes. However, its cost efficiency is yet to be measured in many ways. At the end of the day, patients who are the key stakeholders in the healthcare continuum have to receive accessible, affordable and high-quality Cancer care and our efforts must be driven with this only purpose.