Many applications are being developed to elicit patient history and make comprehensive patient documents by collating laboratory reports and radiological images.
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:
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.
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.
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.
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.”