There is a severe shortage of healthcare workers worldwide, and it’s worsening. The World Health Organization (WHO) predicts that by 2035, there will be a shortage of 12.9 million healthcare workers. This shortage makes it very hard for people, especially in poorer countries, to get the medical care they need, leading to more sickness and deaths.

India faces a significant shortage of healthcare workers, particularly doctors, across urban and rural areas. The country has struggled to meet the World Health Organization's (WHO) recommendations for doctor-population ratios. As of recent statistics:

1. Rural and Urban Shortages: There's a reported 7% shortage of doctors at Primary Health Centres (PHCs) and a staggering 57% shortfall at Community Health Centres (CHCs) across rural and urban areas. This reflects a broader issue of inadequate healthcare coverage, especially in rural regions​. Ref.

2. Specific Shortages in Urban Areas: Urban PHCs face a doctor shortage of about 18.8%, and urban CHCs experience a shortage of nearly 46.9% in specialists. This indicates a severe lack of medical professionals to meet the population's demands in these critical healthcare facilities​. Ref​.

3. Overall Deficit: Across India, there's an overall deficit in healthcare professionals, with specific shortages in surgeons (83.2%), obstetricians and gynecologists (74.2%), physicians (79.1%), and pediatricians (81.6%) at CHCs. This shortage is even more pronounced when considering the total number of healthcare workers needed to meet the ideal WHO standards, including doctors, nurses, and midwives​. Ref​.

4. Historical Context and Progress: Despite efforts to improve healthcare infrastructure and staffing, progress remains slow, especially in rural areas where more than two-thirds of the population resides. For example, the number of doctors at PHCs slightly decreased from 2021 to 2022, compounding the existing challenges in meeting healthcare needs.​ Ref​.

We've poured all the VC money and the tech into new apps and gadgets, but we must start using some of that to tackle real problems, like how so many people in India don't have primary medical care. It's surprising that in today’s world, where we can order almost anything from our phones, folks still can't get primary healthcare, a significant part of our society. AI could be a game-changer here, helping to bring doctors and medical advice to those who are missing out. It’s about ensuring no one It'sto suffers because they can’t see a doctor. We really should be using our tech smarts to fix these significant issues.

Technology presents a promising outlook, mainly through the advances in artificial intelligence (AI). Vinod Khosla, an investor in OpenAI, has shared an optimistic view on the future of AI in healthcare. He believes that within the next decade, AI could provide near-free medical consultations to everyone, essentially serving as near-zero-cost doctors. This prediction hinges on the rapid evolution of AI capabilities, which are expected to dramatically expand access to essential services without incurring the high costs typically associated with healthcare.

Khosla envisions a future where AI not only makes healthcare more accessible but also revolutionizes other sectors like legal services and education by providing free access to lawyers and tutors. His predictions extend even further, suggesting that by 2048, there could be a billion bipedal robots (robots with two legs), potentially transforming numerous industries and societal functions. According to Khosla, these advancements will significantly reduce the need for human labor in these fields, freeing individuals to pursue other interests and enhancing the quality of life globally.

In his discussions, Khosla emphasizes the potential for AI to democratize access to services that are currently expensive and limited to those who can afford them. By reducing the cost barriers and making these services available 24/7, AI could offer solutions to some of the most pressing challenges in today’s global society, especially in healthcare. His venture capital firm, Khosla Ventures, has actively invested in this vision, supporting AI developments that aim to provide widespread benefits and reshape how essential services are delivered worldwide.

Khosla's ideas highlighted we could streamline or eliminate current healthcare inefficiencies such as mandatory physical visits, prolonged waiting times, and procedural redundancy. Khosla speculated on using mobile devices to perform routine checks that would traditionally require a visit to the doctor’s office. And a system capable of processing vast amounts of medical data to deliver personalized healthcare more efficiently than human practitioners. Khosla also critiqued the existing medical education and practice, suggesting that the future should utilize computational tools to handle diagnostic and treatment tasks, thereby freeing human doctors to focus on areas where they add the most value, such as complex decision—making and empathetic patient care.

So, I started exploring the mechanics of creating an AI doctor. Creating AI doctors involves developing sophisticated artificial intelligence systems that can understand, process, and respond to medical queries like a human doctor.

Vinod’ Khosla’s son, Neil Khosla’ Khosla's Sarvam AI, is leveraging generative AI to address healthcare needs specific to India. Sarvam AI has been focused on developing custom model training and establishing a platform for model authoring and deployment. Their approach includes integrating AI into various applications to serve the public good, mainly through voice-enabled interfaces that cater to India’s diverse linguistics. This involves training AI models to accommodate multiple Indian languages and dialects​ ​.

While specific details on the exact algorithms or tools Neil Khosla used to create an AI doctor are not publicly available, his approach involves using generative AI that generates text, audio, and other media formats that mimic human-like outputs. This would typically involve deep learning models and possibly using platforms like Azure, given Sarvam AI's partnership with Microsoft to enhance AI solutions tailored for the Indian context. They aim to augment the capabilities of doctors rather than replace them, enabling more efficient data management and predictive diagnostics. This could involve using machine learning models to analyze vast medical data for better patient management and treatment outcomes​​. Here are more details on machine learning algorithms that process and analyze medical data, predictive modeling, and natural language processing to interact with patients and healthcare providers.

1. Data Collection: The first step is gathering vast India-specific medical data, which will always be a challenge. This data includes patient records, symptoms, diagnoses, treatment outcomes, and medical research findings. It helps the AI learn about aspects specific to Indians.

2. Machine Learning: Using the collected data, machine learning analyzes it to find patterns and learns from them. For example, it learns what symptoms frequently lead to TB (tuberculosis), specific diagnoses, or what treatments are most effective for particular conditions.

3. Algorithm Development: The core of an AI doctor is its algorithms, which make decisions based on learnings. Many specific models are available for the healthcare space, but developers need to write and refine these algorithms to help the AI mimic the diagnostic and decision-making abilities of human doctors.

4. Natural Language Processing (NLP): This technology enables the AI to understand and generate human language. So, when patients describe their symptoms, the AI can interpret what they’re saying, ask relevant questions, and provide advice or a diagnosis.

5. Testing and Training: Before AI doctors can be deployed, they undergo extensive testing to ensure they are safe and effective. This includes simulated scenarios and trials to refine the AI’s understanding and accuAI's.

6. Implementation: Once tested, AI doctors can be implemented in various forms, such as apps, websites, or as part of existing healthcare systems. They can provide immediate medical consultations, help prioritize emergency cases, or assist human doctors by offering a second opinion.

7. Continuous Learning: AI systems can continue to learn and improve over time. They update their knowledge and refine their algorithms as they get exposed to more health cases and the latest medical research.

As mentioned, the specific deep learning models and machine learning algorithms Sarvam AI uses to create an AI doctor aren't detailed in the public domain. However, AI healthcare systems generally utilize the following established models and algorithms. s:

1. Convolutional Neural Networks (CNNs): These are primarily used for image analysis and are especially prevalent in medical imaging, where they help diagnose diseases from X-rays, MRIs, and CT scans. CNNs can detect patterns in imaging data indicative of certain medical conditions.

2. Recurrent Neural Networks (RNNs) are useful for sequential data such as a patient's medical history or time-series data from medical devices. RNNs, particularly their variants like LSTM (Long Short-Term Memory) networks, can predict patient outcomes based on their historical health data.

3. Autoencoders: Used for dimensionality reduction in complex datasets, crucial in medical data processing to extract relevant features that influence diagnostic or treatment outcomes.

4. Decision Trees: This machine learning algorithm makes clinical decisions based on the symptoms and test results inputted into the system. It helps map probable disease conditions from structured data.

5. Random Forests are an ensemble of decision trees typically used for classification and regression tasks. They are robust against overfitting and effective in handling non-linear data, making them suitable for various medical prediction tasks.

6. Support Vector Machines (SVM): Used for classification and regression of both linear and non-linear data. In healthcare, SVMs can classify diseases based on genetic or complex patient data.

7. Gradient Boosting Machines (GBMs): This powerful ensemble technique builds on decision trees. GBMs improve models' accuracy by correcting the mistakes of previous trees in the series.