AI Revolution In Healthcare: From Diagnoses to Efficiency, The Transformative Journey

The impact of Artificial Intelligence (AI) on life sciences and healthcare has been rapidly growing in recent times, with its adoption accelerating in the past decade. The availability of Big Data and AI is helping the industry solve complex problems.

In the health sector, AI is tasked with generating insights across R&D, sales, commercial, diagnoses, operations, marketing, and implementing patient support programs through Market Surveillance and more.

According to a report by Global Data, a data and analytics company, the global market for AI in the medical devices industry is expected to grow at a compound annual growth rate (CAGR) of 29.1 per cent from USD 336 million in 2023 to USD 1.2 billion in 2027.

As per the report, the use of AI has decreased the error rates of cancer detection. A study from 2016 showed that pathologists who utilised AI in detecting cancer-positive lymph nodes had reduced their error rate from 3.4 per cent to 0.5 per cent.

Another report published by Gartner highlighted that by 2025, more than 30 per cent of new drugs and materials will be systematically discovered using generative AI techniques, up from zero today.

Research & Development

Manish Mittal, head of India at Axtria, a cloud software and data analytics company, said the potential of generative AI as a superpower in drug discovery and development is gaining increased acceptance. It supports the drug discovery process by providing relevant scientific and technical information, assisting with the analysis of large datasets, and helping to identify potential new drug targets that will accelerate the speed of treatment. Generative AI has already been used to design drugs for various uses within months, offering pharma significant opportunities to reduce both costs and the timeline of drug discovery.

Precise Diagnoses

In medical imaging, AI algorithms analyse images like X-rays and MRIs with greater speed and accuracy. Stressing this, Suman Bannerjee, CIO at Hedonova, said AI assistance is quicker and makes more precise diagnoses. Robot-assisted surgery is another groundbreaking application of AI in healthcare. The robotic systems, guided by AI, can perform complex surgeries with a level of precision and control beyond human capability, leading to improved surgical outcomes, he added.

Efficient Management

"But there is more than just efficient management. Today, AI models assist doctors in the early detection of patients at high risk of developing complex chronic diseases. By identifying these individuals early on, doctors can initiate timely interventions and treatments, ultimately reducing the burden on patients in terms of surgery care and specialist consultations. This approach holds immense significance for emerging economies like India where access to specialised healthcare services may be limited," said Chaitanya Raju - Executive Director - HealthPlix Technologies.

AI is poised to help medical professionals become more efficient and accurate in diagnosing diseases and providing treatments. Additionally, AI can assist a small population of clinicians in delivering quality healthcare to the next billion.

Adding to that, Tathagato Rai Dastidar, Founder and CEO of SigTuple, said AI is adding immense value to the diagnostic industry, especially in radiology. AI-assisted reads are becoming the norm, and a plethora of companies are coming up with AI solutions for different types of scans. AI for pathology is also coming of age. In both the above fields, the AI-assisted review of samples or scans vastly reduces the review time and is proven to increase accuracy. However, it is also believed that AI in medicine should always act as an assistant to the medical professional and never aim to supplant them.

"In surgical procedures, AI-powered robotic systems enhance precision and reduce errors.  AI can assess tissue thickness in real time, ensuring automatic adjustments during procedures. These innovations, exemplified by companies like Intuitive Surgical, underscore the transformative impact of AI in surgery." - Namit Chugh, Principal at W Health Ventures

Like every other industry, AI also streamlines various administrative tasks in healthcare, such as scheduling, patient data management, and billing, thereby increasing the overall efficiency of healthcare systems. AI's integration into healthcare presents a transformative shift, offering more accurate diagnostics, personalised treatments, and improved efficiency in healthcare services, ultimately leading to better patient outcomes and a more streamlined healthcare system.

The benefits of AI in the health industry are numerous. Certainly, it also comes with underlying challenges in adoption, ethical use of AI and cost pressure.

Challenges

Despite the transformative potential of AI, implementation can be hindered by the challenges posed by large models like Generative AI. Challenges include biases and discrimination (originating from large volumes of data), compliance and privacy issues, lack of transparency, and lack of governance and accountability. Suman Bannerjee said key concerns include data privacy and security risks due to the handling of sensitive patient information. 

Ethical issues arise around AI decisions, especially concerning accountability and transparency. Integrating AI into existing healthcare systems can be complex and expensive, with a risk of exacerbating healthcare disparities among different socio-economic groups. The effectiveness of AI is contingent on the quality and quantity of data, posing challenges in data management. Navigating the regulatory landscape for AI in healthcare is also complex, Banerjee added.

Adoption

While adopting AI into healthcare, professionals and organisations evaluate and adopt AI-powered medical devices based on various crucial factors. "To adopt AI, key considerations include accuracy, reliability, safety, ease of use, and integration capabilities with existing healthcare systems. Additionally, cost-effectiveness and the potential return on investment are pivotal in the decision-making process," said Manish Mittal. Suman Bannerjee noted that there is a skill gap in professionals proficient in both healthcare and AI, and moreover, there is resistance among healthcare providers and patients who are sceptical about adopting AI technologies. He highlighted that about half of US doctors do not want to use AI.

Concerns

AI poses ethical concerns since its emergence, and the debate on ethical use is ongoing. This is also true in healthcare. "Ethical AI use in healthcare demands a holistic approach, starting with leadership commitment and permeating throughout the organisation. Upholding high ethical standards in code creation, emphasising people, empathy, and unwavering commitment to data privacy is crucial," said Manish.

Cost

According to research from Harvard's School of Public Health, leveraging AI for diagnoses could lead to a remarkable reduction in treatment costs, potentially slashing expenses by up to 50 per cent. Manish highlighted that beyond cost savings, the positive impact of AI extends to enhancing access to healthcare, especially for individuals residing in rural or underserved areas. In regions where medical professionals and specialists are scarce, AI technologies can bridge the gap, ensuring that people receive timely and accurate medical attention. This not only improves health outcomes but also contributes to a more equitable distribution of healthcare resources, he added. However, Suman Bannerji mentioned that while AI can reduce hospital costs to patients, the cost to upgrade the current systems would increase by 250 per cent for hospitals.