Artificial intelligence (AI) has the potential to revolutionize healthcare in a variety of ways. For example, it can transform massive amounts of patient data into meaningful information, improve public health surveillance, speed up health responses, and provide leaner, faster, and more targeted research and development.
AI could help in healthcare by mining medical records, making treatment plans, predicting health events, helping with repetitive tasks, doing online consultations, helping doctors make decisions, managing medications, making new drugs, helping people make healthier decisions and choices, and solving public health problems, among other things.
AI in healthcare, in particular, can help clinicians by automating clinical paperwork and image analysis and assisting with virtual observation, diagnosis, and patient outreach. In addition, AI could be beneficial in places like rural and remote areas where few people work.
The following are some of the typical applications of healthcare:
- Robot-assisted surgery,
- Virtual nursing assistants,
- Dosage error detection,
- Clinical trial participation,
- Preliminary diagnosis,
- Automated image diagnosis
Healthcare AI advancements around the globe
The applications of AI in healthcare are divided into three categories:
1. Patient-oriented AI - It improves patient care directly
2. Clinical-oriented AI - It assists physicians in clinical settings and ongoing research
3. Administration and operational-oriented AI
Some of the world's largest firms utilizing AI in healthcare are:
Aidence (Amsterdam, The Netherlands) Clinician-oriented AI for radiologists: improving diagnostics for lung cancer treatment.
Aiva Health (Los Angeles) Administrative and Operational-oriented - The first voice-powered care assistant: connects patients with the appropriate doctor for communication.
Babylon Health (London) Administrative and Operational-oriented - Uses NLP and AI to create an internationally accessible and affordable health system for all.
Bot MD (Singapore) Clinician-oriented - Bot assistant: answers clinical questions, transcribes dictated case notes and automatically organizes images and files.
Suki (San Francisco) Clinician-oriented - Voice-enabled digital assistant for physicians.
Insitro (San Francisco) Patient-oriented - Advanced machine learning and computational genomics are used to lower the time and expense of drug discovery for patients.
India is also developing rapidly in healthcare AI
Here are some of the Indian AI healthcare firms that are revolutionizing the sector:
DocTalk: It is a smartphone application based in Mumbai that allows patients to save their medical papers and history on the cloud. Users can also communicate with their doctors and obtain medications while on the go by using the app. The plan is to develop AI-powered, on-demand virtual assistant software to help simplify the Indian healthcare environment.
Dozee: It is a Bengaluru-based AI healthcare firm. Dozee produces contactless health monitors that silently detect heart, respiration, sleep patterns, stress levels, cardiac contractions, apnea, and other vital signs while sleeping. In addition, its artificial intelligence systems detect any health worsening early.
Niramai: It offers Thermalytix, a device that employs an AI-based high-resolution thermal sensor for the early detection of breast cancer. Cancer screening is now possible in clinics across India because of the automated, low-cost, and portable software-based medical gadgets.
Challenges ahead
The ratio of physicians to patients in India is low; each clinician works between 14 and 18 hours daily. As a result, professionals may overlook the disease's first symptoms due to their heavy workload. However, a computer-aided diagnosis (CAD) system can benefit physicians in detecting these signs at an early stage. For example, researchers from the University of Calcutta said their applied CAD system could detect lung nodules in their early stages, which may signal lung cancer if identified later.
The following challenges are potential obstacles to AI application and growth in India's health sector.
The cost, initial investment, and infrastructure requirements for implementing AI in healthcare are significant barriers.
Dealing with big data, essential for AI-driven healthcare, offers several obstacles, such as many unstructured data sets and interoperability concerns, the lack of available medical data sets, and insufficient analytical tools capable of working with big data.
A lack of AI-trained professionals may also be a significant barrier to implementing AI in healthcare.
Conclusion
In India, particularly in rural regions, access to decent healthcare is frequently a barrier that AI technologies have the potential to relieve. Rural populations in India may even need more basic healthcare amenities. Major corporations, such as Microsoft and Google, have also collaborated on several initiatives to assist in developing AI infrastructure across the country. For example, they have piloted hospital chains in India. Practo, an appointment booking service for patients in India, has also been focusing on using AI to automate patient interactions.
It is recommended that the government should help companies invest in AI, encourage public-private partnerships in the field of AI and health, make and enforce laws and rules about AI and fitness, make policies about confidentiality and privacy in AI-driven healthcare, and set up a certification system for AI-based healthcare solutions. Furthermore, Healthcare workers require AI training to handle sensitive data, secure data and use AI technology.
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