CONTENTS
- Can AI Help in Navigating Mental Health?
- The Need to Curb Black Carbon Emissions
Can AI Help in Navigating Mental Health?
Context:
Current developments show that therapy is now easily accessible through text-based platforms. Natural Language Processing (NLP), a field of Artificial Intelligence (AI), allows computers to understand and interpret human language, akin to human comprehension. In mental healthcare, AI, particularly through NLP, is rapidly expanding its applications, offering affordable therapy options and better support for clinicians.
Relevance:
GS3- Developments and their Applications and Effects in Everyday Life
Mains Question:
What role can Artificial Intelligence play in navigating mental health issues? What are the associated concerns in this regard and what can be done to minimise them? (15 Marks, 250 Words).
Associated Benefits:
Privacy:
Societal stigma surrounding mental health issues persists globally, but NLP programs, operating through text-based platforms and virtual mental health assistants, offer a solution. They provide privacy and anonymity, which encourages individuals to seek help.
Personalised Care:
- These chatbots assist users in reshaping their thoughts, validating their emotions, and delivering personalized care, especially in situations where human support is lacking.
- This not only proves beneficial when direct therapist access is unavailable but also contributes to improved patient health outcomes, comparable to traditional in-person care.
Ensuring Continuity:
- Not only does it aid in enhancing patient health outcomes comparable to in-person care, but it also contributes to the continuity of care needed for a holistic approach to mental health treatment, thereby reducing the likelihood of relapse. For instance, digital therapy assistants can direct individuals to resources for managing distress, grief, and anxiety in moments of need.
- These chatbots, scalable, cost-effective, and accessible round the clock, could be seamlessly integrated into existing health programs.
- Furthermore, companies developing chatbots should actively broaden their service delivery scope through partnerships and collaborations to offer follow-up services like referrals, in-person treatment, or hospital care, as required.
- Addressing one of the significant challenges in mental health treatment—patient adherence to prescribed treatments—AI can predict instances of non-compliance and issue reminders or alert healthcare providers for manual interventions.
- These alerts can be delivered through various channels such as chatbots, SMS, automated calls, or emails.
Assisting Clinicians:
- As for clinicians, mental health conditions often have multifaceted origins, complicating the design of straightforward protocols or quick, accurate diagnoses.
- Utilizing vast datasets, AI tools can summarize information from various sources such as clinical notes, patient interactions, neuroimages, and genetic data.
- This assists clinicians in comprehensively understanding patient histories, thus saving valuable time during sessions.
- Additionally, certain chatbots are developing e-triaging systems that can substantially reduce wait times and release clinical personnel from administrative duties, allowing them to focus more on patient care.
- With the enhancement of bandwidth, mental health providers can allocate a greater portion of their time to severe mental illnesses like bipolar disorder and schizophrenia, which demand specialized care.
Predictive Analysis:
- Recent progress in NLP programs has showcased their ability to anticipate responses to antidepressants and antipsychotic medications through the analysis of brain electrical activity, neuroimages, and clinical surveys.
- This predictive capacity has the potential to streamline treatment decisions and decrease the likelihood of ineffective interventions.
- Moreover, AI can analyze diverse data sources including patient medical records, behavioral data, and voice recordings from intervention services to identify early warning signs of mental health issues before they escalate.
AI Wearables:
- Instead of relying solely on user-initiated interactions through apps, certain AI-driven mental health solutions operate as wearables, utilizing sensors to interpret bodily signals and intervene when necessary.
- They can monitor various physiological parameters such as sleep patterns, physical activity, and heart rate variability to assess the user’s mood and cognitive state.
- This data can then be compared with anonymized data from other users to provide predictive alerts indicating when intervention might be beneficial.
Challenges Ahead:
- Firstly, there’s the issue of AI bias, which refers to inaccuracies or imbalances in the datasets used to train algorithms, potentially resulting in unreliable predictions or perpetuating social prejudices. For instance, if mental health issues are known to be underdiagnosed in certain ethnic groups due to limited access to healthcare, algorithms relying on such data may inaccurately diagnose those issues.
- Additionally, diagnosing mental health conditions often involves more subjective judgment from clinicians compared to diagnosing physical ailments. Similarly, machines tasked with diagnosis face the same challenge. Decisions must be based on patients’ self-reported feelings and experiences rather than objective medical test data, potentially introducing more uncertainty into the diagnostic process.
- A recent report by the World Health Organization highlights significant gaps in our understanding of AI’s application in mental healthcare and identifies flaws in existing AI healthcare applications’ data processing methods.
Way Forward:
- There exists significant potential and promise in these applications, and we anticipate their increasing adoption. Looking ahead, companies should refine these applications by utilizing more diverse datasets representing various populations to mitigate bias.
- These programs could also incorporate a broader range of health indicators to offer a more comprehensive patient care experience. We foresee greater success of these programs if they are guided by a conceptual framework aimed at enhancing health outcomes and undergo rigorous and continuous testing.
- It’s crucial for AI engineers and mental healthcare professionals to collaborate in implementing checks and balances to mitigate these biases or eliminate biased data before it influences algorithmic output.
Conclusion:
In the pursuit of innovation, governments and institutions must prioritize user safety and well-being by ensuring compliance with global standards. As these applications evolve, it is imperative to continually update our beliefs, governing laws and regulations, and strive for higher standards of care.
The Need to Curb Black Carbon Emissions
Context:
The recent developments at the COP26 climate talks in Glasgow, November 2021, saw India commit to achieving net-zero emissions by 2070, positioning itself as a leader in the race towards carbon neutrality. With over 180 GW of renewable energy capacity installed by 2023 and a target of 500 GW by 2030, as per the Ministry of New and Renewable Energy, India is making significant strides towards its renewable energy goals. However, while long-term carbon dioxide mitigation strategies are crucial, efforts providing immediate relief are equally essential.
Relevance:
GS-3- Environmental Pollution and Degradation
Mains Question:
What is black carbon and why is it harmful for the environment? In this context discuss whether the Pradhan Mantri Ujjwala Yojana has helped in reducing the use of traditional cooking fuels in India. (15 Marks, 250 Words).
Black Carbon:
- Black carbon, a potent component of particulate matter, is generated through the incomplete combustion of fossil fuels, wood, and other fuels.
- While complete combustion would convert all carbon in the fuel into carbon dioxide (CO2), this process is never entirely efficient.
- Consequently, carbon monoxide, volatile organic compounds, organic carbon, and black carbon particles are also produced during combustion. The resulting mixture of particulate matter is commonly referred to as soot.
- Black carbon (BC) is a relatively short-lived pollutant but is the second-largest contributor to global warming, following carbon dioxide (CO2). Unlike other greenhouse gas emissions, BC is swiftly removed from the atmosphere if emissions cease.
- Deposits of black carbon have two primary effects that accelerate glacier melt: firstly, by reducing the surface reflectance of sunlight, and secondly, by elevating air temperatures.
- In India, the primary sources of black carbon emissions are traditional cookstoves burning biomass like cow dung or straw.
- Studies indicate that the residential sector contributes 47% of India’s total black carbon emissions, followed by industries at 22%, diesel vehicles at 17%, open burning at 12%, and other sources at 2%.
- Exposure to black carbon has been linked to increased risks of heart disease, birth complications, and premature death.
- While efforts to decarbonize the industry and transport sectors have led to reductions in black carbon emissions over the past decade, addressing emissions from the residential sector remains a significant challenge.
Pradhan Mantri Ujjwala Yojana (PMUY):
Achievements:
- In May 2016, the Indian Government introduced the Pradhan Mantri Ujjwala Yojana (PMUY), aiming to provide free liquefied petroleum gas (LPG) connections to households below the poverty line.
- The primary goal was to offer clean cooking fuel to rural and impoverished households, thereby reducing their reliance on traditional cooking fuels.
- PMUY not only provides LPG connections but also includes infrastructure such as free gas stoves, LPG cylinder deposits, and distribution networks.
- Consequently, the program has played a significant role in reducing black carbon emissions by offering a cleaner alternative to traditional fuel consumption.
- As of January 2024, PMUY has facilitated connections to over 10 crore households.
Shortcomings:
- However, data from 2022-2023, obtained through RTI queries, revealed that 25% of PMUY beneficiaries, equating to 2.69 crore individuals, either did not refill their LPG cylinders or only did so once, indicating their continued reliance on traditional biomass for cooking.
- An investigation by The Hindu in August 2023 further revealed that the average PMUY beneficiary household consumes only 3.5-4 LPG cylinders annually, compared to the six or seven cylinders typically used by non-PMUY households.
- This indicates that up to half of the energy needs of PMUY beneficiary households are still met by traditional fuels, which emit high levels of black carbon.
- Moreover, the shortage of LPG and increased usage of traditional fuels disproportionately affect women and children, exposing them to elevated levels of indoor air pollution, leading to various health issues and premature deaths.
Way Forward:
- The primary means of improving living standards in these regions hinges on ensuring access to clean cooking fuels.
- While renewable energy sources offer long-term solutions for rural energy needs, immediate benefits for rural communities are anticipated through the use of LPG.
- In October 2023, the government raised the LPG subsidy from ₹200 to ₹300. However, despite this increase and considering the substantial rise in LPG prices over the past five years, the cost of a 14.2-kg LPG cylinder, even with the additional subsidy, remains around ₹600 per cylinder.
- Many PMUY beneficiaries find this price prohibitively high, especially when alternatives like cow dung and firewood are readily available at no cost.
- The Prime Minister announced a further ₹100 price reduction in March 2024, though this subsidy is expected to be temporary.
- The government estimates that around ₹12,000 crore will be allocated for PMUY subsidies in 2024-2025, a figure that has been consistently rising each year since the scheme’s inception.
- While it is the government’s responsibility to make clean fuel affordable through subsidies, the issue of low refill rates will persist if availability challenges are not addressed.
- Another significant obstacle to the success of PMUY is the lack of last-mile connectivity in the LPG distribution network, leading to remote rural areas relying predominantly on biomass for cooking.
- One potential solution to this challenge is the local production of coal-bed methane (CBM) gas through biomass composting.
- CBM is a cleaner fuel with lower black carbon emissions and requires investment. Panchayats can take the initiative to locally produce CBM gas at the village level, ensuring access to clean cooking fuel for every rural household.
Conclusion:
As India fulfills its commitments on the international platform toward long-term decarbonization, immediate action is imperative. Prioritizing the reduction of black carbon emissions, notably through initiatives like the PMUY scheme, can position India as a global leader in addressing regional health issues and contribute to achieving its Sustainable Development Goal of ensuring affordable clean energy for all while aiding global climate mitigation efforts. Recent estimates suggest that mitigating residential emissions could prevent over 610,000 deaths annually.
