In a recent conversation, Rohit Ghosh, a distinguished expert in healthcare AI and former executive at Cure.ai, shared his profound insights into the game-changing role of artificial intelligence in modern healthcare. His journey, from diverse fields like investment banking and logistics to the forefront of healthcare AI, highlights the powerful impact of innovative technology on improving medical practices and patient outcomes worldwide.

A Journey Towards Healthcare Transformation

Ghosh’s transition to healthcare AI was driven by a desire to make a lasting difference. After a brief stint in industries unrelated to healthcare, he recognized his passion for leveraging advanced technology to save lives. This realization led to the co-founding of Cure.ai, where Ghosh dedicated eight years to developing AI-driven solutions aimed at addressing some of the biggest challenges in healthcare.

The Integration of AI in Healthcare and Its Benefits

The integration of AI into healthcare systems has been particularly effective in the field of medical imaging. Ghosh explains that AI has three primary benefits in radiology:

1. Enhanced Efficiency: Traditionally, radiologists spend significant time analyzing medical images like CT scans and MRIs, which can take up to 30 minutes. With AI-powered pre-analysis, this time is drastically reduced, allowing radiologists to focus on the most critical areas.
2. Improved Accuracy: AI excels in detecting minute details that might be missed by the human eye. Whether it’s a tiny nodule in the lungs or a subtle brain bleed, AI improves the precision of diagnosis by complementing human expertise.
3. Early Detection: One of the most significant advantages of AI is its ability to catch incidental findings, such as detecting early-stage cancer in patients who are being scanned for unrelated issues. Early detection can lead to life-saving interventions.

Reducing Healthcare Inequities

One of the most profound impacts of AI, according to Ghosh, is its ability to reduce healthcare disparities, especially in underserved and rural areas. AI can democratize access to high-quality care by enabling timely diagnoses, even in remote locations:

• Rural Hospitals: In areas where radiologists aren’t available around the clock, AI can provide immediate diagnostic support for urgent conditions like strokes.
• Tuberculosis: AI-driven diagnostics can reduce the time needed for tuberculosis diagnosis from weeks to just one hour, a critical improvement in areas where the disease is prevalent.
• Remote Care: AI bridges the gap between rural and urban healthcare, ensuring that patients in remote locations receive the same standard of care available in major cities.

Overcoming Challenges

While the potential of AI in healthcare is immense, there are several challenges that must be addressed to fully realize its benefits:

• Awareness Gap: Many healthcare practitioners are still unaware of the full capabilities and limitations of AI in medical applications.
• Evaluation Expertise: Hospitals and healthcare providers need clear guidelines on how to evaluate and adopt AI solutions effectively.
• Policy and Regulation: Policymakers must keep pace with technological advancements, ensuring that regulations support innovation while safeguarding patient care.

Future Trends in AI and Healthcare

Looking ahead, Ghosh highlights several promising trends in healthcare AI:

• Personalized Medicine: AI is enabling the discovery of new biomarkers, leading to more targeted and individualized treatments.
• Preventive Healthcare: The shift from reactive care to proactive health monitoring will allow for earlier interventions and better health outcomes.
• Drug Discovery: AI is accelerating the development of new drugs, as well as optimizing combinations of existing medications for improved efficacy.

Building a Sustainable AI Ecosystem

For AI to truly revolutionize healthcare, collaboration among key stakeholders is essential. Policymakers, technologists, and healthcare professionals must work together to build a sustainable ecosystem that promotes innovation while ensuring that AI-powered healthcare solutions are accessible to all. Proper implementation, equitable access, and forward-thinking policies are the cornerstones of a future where AI plays a central role in patient care.

The journey of integrating AI into healthcare is a long but rewarding one. Persistence and collaboration will turn today’s innovations into tomorrow’s standard practices. AI’s potential to enhance healthcare is not just about technology, but about transforming the way care is delivered, especially in regions that need it most. By continuing to push the boundaries of AI, the healthcare industry can achieve a more equitable and efficient system, providing high-quality care for all.

Authors Biography

Rohit Ghosh, an IIT-Bombay graduate and Founding Member of Qure.ai, specializes in business development and strategy. A Google Developer Expert in Machine Learning, he has 15+ publications, including in The Lancet. Rohit mentors data science students, collaborates with GreyAtom, and speaks at global conferences on AI and ML.

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Artificial Intelligence (AI) enables a machine to mimic human brain functions, albeit faster, more consistently and more accurately. AI has already brought a metamorphosis into the operation of many industries. It is now bringing the same revolution in the field of healthcare, propelled by the increased availability of authentic digitalized healthcare data. AI integrates advanced analytics and machine learning to interpret and analyze data to give concrete and reliable output, and that too in a fraction of a minute!

AI is becoming omnipresent now, and in the field of healthcare, it has a variety of applications. For example, AI-based applications are assisting doctors in writing medical notes by voice recognition. Many applications are being developed to elicit patient history and make comprehensive patient documents by collating laboratory reports and radiological images. AI is also being used by medical insurance companies to scrutinise patient claims; AI applications are being implemented in hospital operations for supply chain management and bedside management.  AI in healthcare can be classified into various categories based on its applications and functionalities:

1. Operational:

AI impacts the operations of healthcare organizations in several ways.

Workflow optimization: AI can streamline hospital operations, manage patient data, schedule appointments, and optimize patient flow, resulting in improved efficiency.

Supply chain management: AI can be utilized in predicting demand, managing inventory, and eventually, even automating procurement processes to supplement the hospital’s procurement processes.

2. Diagnostic:

AI has the maximum contribution in the field of diagnostics due to the availability of a large quantum of digitalized data. Some of the applications are:

Medical imaging: AI algorithms analyze medical images, such as X-rays, CT scans, and MRIs, to identify abnormalities and detect diseases more accurately and quickly. This category of AI tools has the largest number of clearances from the US FDA.

Early disease detection: AI solutions can identify patterns in large datasets to recognize early signs of ailments, including cancer, diabetes, and neurological disorders.

3. Curative:

This is an area in which AI is still being tested. AI can be applied in:

Personalized medicine: AI-driven tools analyze patients’ genetic, clinical, and lifestyle data to develop tailored treatment plans and recommend appropriate therapies.

Predictive analytics: AI can quickly analyze enormous amounts of complex data and hence can be utilized to predict disease outbreaks, patient outcomes, and the likelihood of complications through pattern identification.

4. Augmenter:

The following are some of the recent applications of AI in this category:

Virtual health assistants: AI-powered chatbots and voice assistants can provide personalized health recommendations, medication reminders, and symptom assessments, supporting patients in managing their health better.

Mental health: AI solutions can analyze patients’ speech, text, or behavior patterns to detect mental health issues, such as depression or anxiety, enabling early intervention and treatment.

Most of the growth of AI in healthcare has been in the field of diagnostics. The advantage that AI-based applications bring to the field of medical diagnostics is multi-fold. Firstly, it makes the diagnostic facility widely accessible and more affordable by assisting the healthcare personnel in delivering quick and accurate results. Secondly, AI can take care of all the mundane tasks like inventory management and back-end paperwork so that the paramedical/nursing staff is free for active patient care. Thirdly, AI can help in optimal resource utilization and improve the efficiency of every diagnostic modality. 

Fourthly, AI can process data and medical images at a faster rate and identify patterns and diseases which might be missed by the human eye. With these advantages of AI, the quality of healthcare improves drastically, and patient mortality and morbidity can be reduced to a great extent. Last but not the least, with the help of AI applications, the issue of deficiency of trained paramedical manpower in rural areas can be addressed effectively.

With the help of AI applications, the issue of deficiency of trained paramedical manpower in rural areas can be addressed effectively.

Diagnostic AI Startups in India:

India has witnessed a surge in AI-based healthcare solutions in recent years, driven by a growing need for accessible and cost-effective medical services. While system-wide adoptions may still have a long way to go, several notable AI implementations in the Indian healthcare sector are:

1. SigTuple: It is a Bengaluru-based start-up that uses AI to analyze pathological images and lab reports to assist doctors in diagnosing patients. SigTuple’s pioneering product, Manthana, is a diagnostic intelligence platform that helps pathologists quickly and accurately identify diseases.

2. Niramai: Another Bengaluru-based start-up, Niramai uses AI to help in the early detection of breast cancer. Niramai’s solution is an AI-powered platform, Thermalytix, which uses thermal images to detect breast cancer in its initial stages, thereby providing a non-invasive diagnostic modality.

3. Qure.ai: A Mumbai-based start-up, Qure.ai uses AI to assist radiologists in diagnosing and interpreting medical images. The company’s flagship product, qXR, uses deep learning algorithms to analyze X-ray images and identify potential abnormalities.

4. Oncostem: A Bengaluru-based start-up, Oncostem has developed innovative prognostic tests that evaluate the aggressiveness of tumors based on in-depth knowledge of tumor biology to determine the unique characteristics of cancer recurrence risk. These functional tests aid physicians in curating a customized treatment plan. 

5. Artelus: Artelus is another Bengaluru-based start-up that saves lives by quickly screening for diseases like tuberculosis, breast and lung cancer, and Diabetic Retinopathy (DR), where early detection makes a world of difference. For instance, DRISTi, its deep learning-based AI-powered algorithm, reads digital images to detect and identify early signs of DR during a simple eye check-up without a hospital setup.

6. Tricog: Tricog is yet another Bengaluru-based start-up which believes that remote cardiac diagnosis is an effective way to empower healthcare providers. With robust AI technology backed by human expertise, Tricog is a forerunner in its chosen medical technology space, with its InstaECG and InstaECHO, which are solving life-threatening cardiovascular conditions by identifying them in time and accurately.

AI in Diagnostics is not just restricted to startups. Many leading hospitals are also employing AI applications for diagnosis in their day-to-day operations. A relevant example is that of the Madurai-based Aravind Eye Hospital. It has partnered with Google and developed a Machine Learning (ML) algorithm which is being used for the detection of Diabetic Retinopathy. Another example is Chennai-based Sankara Eye Foundation, which has partnered with Singapore-based Lebencare and developed Netra.ai. Netra.ai is a cloud-based AI solution used for the diagnosis of Diabetic Retinopathy. 

Today we see that AI applications are already helping Radiologists / Pathologists / Cardiologists, and multiple other Doctors in diagnosing and treating diseases early and in a better way. AI solutions are supposed to look deeper than what the human eye can see. Consequently, they promise to usher in a new era of improved medical diagnostics that will benefit all stakeholders in healthcare.

As Mahatma Gandhi said, “A correct diagnosis is three fourth the remedy.”

“Composed by: Anumeha is Chief Customer Officer at Qure.ai, a deep-tech startup in healthcare. She has worked across business growth, operations, disability inclusion and chronic disease rehabilitation in the past.

“Shweta Jaiswal is a senior Healthcare Professional with over 18 years of experience as an Anaesthetist & Intensivist. She has worked across India in various tertiary care hospitals. Her field of expertise is Cardiac & Neuro Anaesthesia and Cardiac Critical Care & Transplant Critical Care.

“DVR Seshadri is a Professor of Practice in the Marketing Area at the Indian School of Business. His areas of interest are healthcare, business-to-business marketing, and climate change.”

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