Category: Artificial Intelligence

10 Dec 2018
Dr. Nobijith Roy

Artificial Intelligence in Public Health Screening

A Report, based on the Keynote Speech delivered
– By –
Dr. Nobhojit Roy, Team Lead, Health Systems Strengthening, State Resource Unit, CARE-India, Bihar
@ Artificial Intelligence in Radiology 2018 Symposium, November 10, 2018
Organized by Telerad Tech and Image Core Lab


India’s Technology March

In the recently concluded AIR Symposium 2018 in Bengaluru, Dr. Nobhojit Roy presented some rather interesting views on AI in his Keynote address. In his opening remarks, Dr Nobhojit Roy expressed immense pride in India’s Information Technology (IT) journey and especially the lead that it has taken in the space of Artificial Intelligence and machine learning.  In the same breath, he also felt that application of AI in public health screening is still conceptual and nascent, and remains to be tested on the ground.

Availability of Drugs in a Public Health System is a challenge

Availability of drugs at the point of care is important for addressing public health challenges such as tuberculosis (TB) in India. It is often experienced that a patient gets diagnosed with TB in a peripheral facility, but the facility does not have the required medicines. As a result of this, the patient is forced to travel to district hospitals or any other higher centers. Dr Nobhojit felt that availability of drugs and well-equipped diagnostic facility at the primary care level is a pre-requisite for the success of flagship programmes such as the Ayushman Bharat (the National Health Protection Scheme). There would be no point in having a diagnosis and not being able to link it to the essential drug list.

Expressing his concern, Dr. Roy said, that linking diagnostics to the drug list and making it available all under one roof seemed to be the real challenge. However, he was optimistic that availability of medicines was getting better on ground and that the essential medicines lists were getting linked with the health & wellness centers and at the Primary Health Center (PHC) level. He added that 7 diagnostic tests at the sub health center level were being promised while at the PHC the situation had improved to provide up to 90 diagnostic tests. Artificial intelligence would have a very vital role to play in Supply Chain Logistics for drug warehousing and supply, but needs to be based on a robust database. Current platforms of e-aushadhi are being used sparingly by the EAG states.

Manpower Training

In order to improve their skill sets, most of the Physicians go for overseas training at prestigious schools, return and perform successful surgical procedures here. Recovery during post-surgical care is usually handed over to the nurses who been through inadequate training courses in nursing. The surgical outcomes is dependent on the weakest link in the chain – the inadequately trained nurses and not on the highly trained physicians. India seems to lag behind here as we need to approach the systems-approach over the individual-centred approach.

Dr. Roy expressed his concern, stating that even at the PHC level, there are 69% vacancies to be filled in many states. The manpower at these centres are primarily a ANM nurse. Setting expectations, he said that the aim should be to push for an MBBS degree, as a bare minimum to improve consulting at that level. The patient should also receive a data warehouse of education material and patient care pathways which are being developed to aid in the proper treatment and diagnosis. Dr. Roy said that Accredited Social Health Activists (ASHA) are receiving a 5-day training on universal screening of Non-Communicable Diseases (NCDs) while Multi-Purpose Workers (MPWs) are being trained for 4-days on universal health screening. However, he felt that it would be a herculean task to train more than 1,20,000 of ASHA workers being present on ground.

Teleradiology – On ground realities

Post evaluation of all telemedicine projects around 14 states in India, Dr. Nobhojit Roy, was confident that Teleradiology was the best operational model. He expressed satisfaction that all image-based Teleradiology applications were doing well on ground. At the same time, however, he mentioned that it should be linked back to other systems. Not linking it back would mean creating a weak link in the overall function of the health system, using telemedicine.

Having enrolled in a Public Private Partnership (PPP) mode across 9 states – AP, Meghalaya, Rajasthan, Tripura, West Bengal, Assam, Odisha, Uttar Pradesh, and Uttarakhand, Dr. Roy stated that Teleradiology was doing reasonably well. He assured that the government was supportive of CT scans services at the district level hospitals providing full technical support across 24 states – 13 under the PPP module and the rest 11 under the in-house trust state model. Realizing that the out of pocket expenditure on diagnostics is high, financially, the government had provided US$ 172,937,239 (INR 1218 crores) to these 33 states for a free drug- free diagnostic initiative. The concurrent Biomedical Equipment maintenance program is important alongside, to reduce downtime and to be able to provide consistent diagnostic services to the population.

NCT PPP module – the future

Dr. Roy was optimistic that the government was taking steps to ensure that certain services are made available to the public under the Public Private Partnership (PPP) model. For eg; availability of mammography, biopsies at the diagnostic level is being envisaged as the rollout for the program; As a part of the aspirational plan, Cardiology- coronary angiography would be available at the District hospitals under Ayushman Bharat PMJAY scheme, and for Pulmonology- clinical examination during screening and PCG, bronchoscopy x-ray for diagnostics.

He lauded the ministry of health for having partnered with NICE (National Institute of Clinical Excellence), known for making the best guidelines in the world. Over the last three and a half years, jointly, they have come up with standard treatment guidelines for the district hospital. These guidelines will surely make all the health workers well aware of standard treatments and will help in overcoming ad-hoc treatments based on personal belief systems. The patient flow pathway algorithms are a part of it including patient education material. These may serve to build our algorithms for AI, if implemented well.

Adding to this. Dr. Roy mentioned that pathology pap smear was very amenable to AI. And this was a clear example of how AI can be looked at. The productivity of the pathologists can be enhanced using AI in this sphere. With a limited number of pathologists available to be able to look at all these slides that are likely to be generated by screening, could both time-consuming as well as tedious.  So, it would make sense to get at least the initial screening aided by AI.

Like-wise in the case of sputum microscopic, Dr. Roy hoped that where there are already various models in play, there needed to be capabilities of tying up teleradiology with tele-pathology. Dr. Roy believed that this was the space where it would pose a real challenge because according to him most image-based scans, even the retinal scans were proficient for mass screenings. Ethical issues with regards to data sharing will remain and will accompany the regulatory aspect of it. Real ethical and legal challenges notwithstanding, AI can potentially score in electronic health records, big data, epidemiology outbreak, and crowdsourcing. The aim should be augmented intelligence and it is multidisciplinary as the industry needs to get the doctors, data engineers, legal and social scientists, and the logisticians together. There is also the need for a strong curriculum to handle big data as the industry lacks in the same and thus it can’t be used effectively, Dr. Roy concluded.

05 Dec 2018

Artificial Intelligence in Healthcare

A Report based on the Keynote Speech delivered
– by –
Dr Anurag Agrawal, Director, Institute of Genomics & Integrative Biology, CSIR
@ the Artificial Intelligence in Radiology 2018 Symposium, November 10, 2018
Organized by Telerad Tech and Image Core Lab

In the recently concluded, AIR  Symposium 2018 held in Bengaluru which also witnessed the launch of Telerad Tech’s MammoAssist – the AI Software for Early Stage Breast Cancer Detection, Dr. Anurag Agrawal, Director, Institute of Genomics & Integrative Biology, CSIR, expressed his views on Artificial Intelligence (AI) and its game changing paradigm that will help cover the wide gap between the demand and supply sides in healthcare, specially radiology. The topic of his address was “AI in Healthcare”.

Augmented Intelligence or Intelligence Augmentation

Dr. Agrawal said that AI was not new and that it had been brought to light earlier as well. However, the computers earlier could not really implement the true benefit of AI. Over the years, the computational power and the advent of the digital revolution, has given the healthcare sector enough digital data to make these algorithms meaningful. And the potential for AI to completely upend the way the industry delivers healthcare.

Simply put, AI could be defined as making machines think like humans and machine learning was a specific aspect of artificial intelligence. However, he put it in an interesting perspective on augmented intelligence vis-à-vis intelligence augmentation which was not necessarily through machine learning. Bringing in some interesting comparisons, Dr. Agrawal, highlighted key deliverables like AI could work tirelessly for 24×7, could be available instantly to everyone, help in continuous updation, duplicate and grow without ageing – something that humans would lack when compared to these intelligent systems.

Complexity of data surge

Highlighting the limitations of the human mind, Dr. Agrawal stated that, a human mind could only hold five to seven important pieces of information at a time. If that were true, we would soon be approaching limitations of the same and the root cause of our problems in these big data settings would be complexity. As per Dr. Agrawal, a lot would depend on the way we defined our problems in healthcare and its applications. As we acquire more and more data, the size and complexity would defy ease of any categorization. In fact, they couldn’t be even be summarized in human language, accordingly, couldn’t be described and hence humans couldn’t be expected to provide solutions to these problems. For instance, AI could tell the age, gender, detect diabetes, identify if the person was a smoker, show blood pressure and could also predict cardiovascular risks.

To drive home his point, Dr. Agrawal expressed that, through available technologies one could create new systems of prognosis, detection and stratification that have never been done before. However, the doctors themselves would have to let it go, for the field to fully emerge. He highlighted, that the challenge in our country would be posed by the data required to reach accuracy levels – 15,000 images for 80% accuracy to around 40,000 images to achieve over 90% or accuracy.

Dr. Agrawal opined that by creating a competitive market environment annotated data could be made available for free. This could lead to achieving accuracy by reading fewer images. For instance, in the field of deep echocardiography, in only about 100-200 images, the machine was able to read LVH on echocardiograms with 92% accuracy. Invented in 2016, this is a technique called genetic adversary of networks, which creates synthetic images. He explained that a generator in this case, would generate an image which it thought was an echocardiogram, while another network adversarial would check it at the same time. By the time one would reach the last excess post-exercise oxygen consumption (EPOC) of the process, without looking at the annotated image of Left Ventricular Hypertrophy (LVH), the machine would know what an echo was and what a relevant view would be. The learning would then be transferred and with labeled data, one could do a good classification. He lauded, IIT Kharagpur for doing some exceptional work in intravascular ultrasound in this area.

AI and its benefits

Dr. Agrawal expressed that the field of medicine has always been the last one to see any transformation of technology, while fintech has always been the first. In the field of finance, people were implementing AI even before people in the field of medicine were thinking about it. He stated that in the current scenario, AI was already everywhere – in genomics, the machine could dish out lots of relevant stuff, even in radiology, AI was fast becoming the backbone.

Citing examples and comparing them to humans, Dr. Agrawal, said that, AI could bring about positive changes in the healthcare domain. It could describe, classify, predict things and take a step ahead to prescribe things as well for patients. The machines could tell us what to do in order to make a patient better. However, he mentioned, that the most complex things could not be managed by machines alone and would need human interventions.

  • Describing things – when a doctor listened to a patient, the focus of the doctor would only be on the part that s/he specializes in as s/he thinks that would be relevant to her/him. S/he would not really be listening to other areas of problems of the patient. However, a machine had the capability to listen to the entire conversation, filter out information about the patient and create them for other relevant doctors.
  • Classification – All possibilities exist in clinical diagnosis for basic classifications to be taken care of. For instance, derma level skin cancer detection.
  • Prediction – AI lacks the understanding of the likelihood of patients as they walk-in. Dr. Agrawal expressed that we were talking about things we were aware of and not even considering things that we were not aware of and things we didn’t even know existed. Could AI help us with that? Giving an example, Dr. Agrawal highlighted that imagining a human doctor trying to read an MR with 5000 pictures, was simply impossible.


AI – the future game changer

Expressing hope, Dr. Agrawal said, that in the future all the dirty, ultra-complex and background work would be done by machines and AI leaving doctors to do ‘the’ one thing that only they can do and AI cannot – talking to the patient, helping them heal through conversations and informing them properly. He emphasized this human interaction and its importance.

Citing some trends in the field of medicine, Dr. Agrawal said, that AI would impact healthcare services and its quality positively, unlike in the field of biology, as the spirit of sharing existed, and many things would be freely distributed.

He cautioned that regulatory issues would be critical in the future, as we would not want them to become bottlenecks and we would also not want people to be unprotected. As per him, acceptability by consumers and the liability in case of error were a few critical things that required discussions and clarity.

Highlighting the physical patient ratio in our country, Dr. Agrawal hoped that, AI would minimize the impact of low physical patient ratio and prove to be a big game changer, as it would help shift from hospitals to home as a point of contact and reduce the rush at the hospitals. This would essentially lead to shifting from episodic encounters to bringing about continuous surveillance.

In his closing remarks, Dr. Agrawal said that, AI should not be posed as a threat and that it would be positive in the field of medicine bringing out changes required to improve healthcare. However, he warned that, people should not take it lightly, as AI had the potential to replace people who were not communicating properly, were simply in a supervisory role and who would simply make a classification judgment, as AI would make them irrelevant.

viagra satış cialis satış viagra sipariş novagra sipariş yabancı bahis siteleri kadıköy escort