The real question is not whether Macaulay failed India, but whether India’s own elites failed to fulfil even the limited emancipatory possibilities that colonial modernity, however imperfectly, made available. In recent years, Thomas Babington Macaulay has been recast as a principal villain in contemporary Hindutva discourse. His alleged misdeeds are said to lie in the educational system he inaugurated – an arrangement portrayed as having crippled Hindu civilisation until its supposed recent “liberation.” It is doubtful that most of Macaulay’s detractors have read his writings; even among those who have, selective quotation is the norm. At the other end of the spectrum stands another constituency that elevates Macaulay to the status of a pioneer of social justice. Both these narratives obscure more than they illuminate. A historically grounded assessment requires a closer look at the pre-colonial educational landscape and at what Macaulay actually argued. The pre-colonial educational context Before indicting Macaulay, it is essential to understand the state of indigenous education in early nineteenth-century India. The observations of the collector of Bellary, recorded in the 1820s and often cited approvingly by scholars such as Dharampal, are instructive. He noted that Telugu and Kannada instruction depended heavily on literary forms of the language that bore little resemblance to the vernaculars actually spoken: “The natives therefore read these (to them unintelligible) books to acquire the power of reading letters… but the poetical is quite different from the prose dialect… Few teachers can explain, and still fewer scholars understand… Every schoolboy can repeat verbatim a vast number of verses of the meaning of which he knows no more than the parrot which has been taught to utter certain words.” In short, comprehension was minimal; rote memorisation was paramount. Many teachers themselves lacked understanding of the texts they taught. The subject matter was similarly circumscribed. Campbell has recorded that students from “manufacturing castes” studied works aligned with their sectarian traditions, while Lingayat students studied texts considered sacred. Beyond religious material, instruction included rudimentary accounting and memorised lists – astronomical categories, festival names, and the like. The renowned Amarakosha was used largely for its catalogues of synonyms, including names of deities, plants, animals, and geographical divisions. Caste and educational access The sociological profile of teachers and students further reveals the exclusivity of the system. Frykenberg, in his seminal work on education in South India, noted that Brahmins dominated the teaching profession in the Telugu region, while Vellalas did so in Tamil areas. Students overwhelmingly came from upper castes. Frykenberg explains that hereditary occupations, the sacralised exclusivity of high-caste learning, and the financial burden of even modest fees made education virtually inaccessible to the majority. A fee of three annas a month was beyond the reach of many “clean caste” families, let alone the “unclean” – Paraiyar, Pallar, Chakriyar, Mala, Madiga and other communities who constituted close to half the population. Even within the classroom, caste segregation was strictly maintained. If this was the state of affairs in the comparatively less feudal, ryotwari regions of South India, the situation in North India –dominated by zamindari and entrenched feudal relations – can only be imagined. The conclusion is unavoidable: before the advent of British rule, formal education was functionally restricted to privileged groups. Re-reading Macaulay’s minute Against this backdrop, Macaulay’s 1835 minute must be understood. His rhetoric was undoubtedly steeped in imperial arrogance, and he dismissed Indian literary and scientific traditions with unwarranted disdain. Yet the substantive debates of the era did not concern the desirability of mother-tongue instruction; that idea had virtually no advocates at the time. The controversy revolved around whether Sanskrit, Arabic, or English should serve as the medium for higher education. Macaulay argued that: “All parties seem to be agreed… that the dialects commonly spoken… contain neither literary nor scientific information… until they are enriched from some other quarter.” He noted further that despite the state’s investment in printing Sanskrit and Arabic works, these books remained unsold, while English books were in high demand. Thousands of folios filled warehouses, unsought and unused. Meanwhile, the School Book Society sold English texts in large numbers and even made a profit. His infamous proposal to create: “a class of persons Indian in blood and colour, but English in tastes, in opinions, in morals and in intellect” must be read in conjunction with his expectation that this class would subsequently transmit modern knowledge into the vernaculars, rendering them, over time, suitable for mass education. Whether this expectation was realistic or sincerely held is debatable, but the stated logic is unambiguous: English was intended as a bridge for elite modernisation, not the permanent medium of education for India’s masses. The greater tragedy lies not in Macaulay’s intention but in the fact that, 190 years later, vernacular languages have still not been fully equipped to serve as robust vehicles of modern scientific knowledge. Elite demand for English education It is also historically erroneous to claim that English education was imposed against the wishes of the populace. In the Madras Presidency particularly, demand for English education was strong. The 1839 petition signed by seventy thousand individuals, including Gazulu Lakshminarasu Chetty, Narayanaswami Naidu, and Srinivasa Pillai, explicitly requested that English education be introduced without delay. Their petition asserted: “If diffusion of Education be among the highest benefits and duties of a Government, we, the people, petition for our share… We ask advancement through those means which will best enable us… to promote the general interests of our native land.” Similarly, the Wood’s Despatch of 1854 – the so-called Magna Carta of Indian education—stated unequivocally that education should be available irrespective of caste or creed and reiterated the expectation that Indians themselves would carry modern knowledge to the masses through vernacular languages. Practice: Liberal principles, exclusionary outcomes Despite the ostensibly universal language of the 1839 petition, the actual practice in Madras was exclusionary. The standards set for admission to higher education ensured that only the highest castes could qualify. The rhetoric of liberalism facilitated an elite project: by advocating “higher branches of knowledge,” the curriculum implicitly excluded those without prior linguistic and cultural capital. Thus, liberalism provided a vocabulary for political demands while simultaneously enabling the marginalisation of the very groups whose support had made those demands politically effective. Dalit entry into schools frequently required direct resistance to entrenched social norms. The well-documented case of Father Anderson, who was pressured to expel two Dalit boys yet refused to do so, illustrates the uphill struggle faced by marginalised communities across the region. Did Macaulay promote social justice? The British educational system, however limited in intent, did expand opportunities for groups previously excluded from formal learning. The evidence is overwhelming: literacy and access to education grew significantly during colonial rule, whereas pre-colonial systems were highly restricted. But this expansion was an unintended byproduct of administrative rationalisation and economic modernisation – not a deliberate project of social justice. Macaulay himself was no egalitarian. His speeches against the Chartists in Britain reveal his deep opposition to universal suffrage. He famously declared: “The essence of the Charter is universal suffrage… If you grant that, the country is lost.” He compared extending rights to working-class Britons to opening granaries during a food shortage – an act he described as turning “scarcity into famine.” His analogy to starving Indian peasants begging for grain, whom he would refuse even “a draught of water,” reveals a worldview firmly rooted in class privilege and imperial paternalism. Conclusion Macaulay was, unquestionably, an imperialist dedicated to advancing British interests and the interests of his own class. His project sought to cultivate an Indian elite that would perpetuate colonial governance and ideology. That elite did emerge, and it is this class – not Macaulay – that bears responsibility for failing to democratise education and modern knowledge. To depict Macaulay either as the destroyer of an egalitarian indigenous utopia or as a hero of social justice is historically unsustainable. He was neither. He was an articulate functionary of empire whose policies interacted with existing social hierarchies in complex ways – sometimes reinforcing them, sometimes inadvertently weakening them. The real question is not whether Macaulay failed India, but whether India’s own elites failed to fulfil even the limited emancipatory possibilities that colonial modernity, however imperfectly, made available. Source of the article

How is AI and data leadership at large organizations being transformed by the accelerating pace of AI adoption? Do these leaders’ mandates need to change? And should overseeing AI and data be viewed as a business or a technology role? Boards, business leaders, and technology leaders are asking these questions with increasing urgency as they’re being asked to transform almost all business processes and practices with AI. Unfortunately, they’re not easy questions to answer. In a survey that we published earlier this year, 89% of respondents said that AI is likely to be the most transformative technology in a generation. But experience shows that companies are still struggling to create value with it. Figuring out how to lead in this new era is essential. We have had a front row seat over the past three decades to how data, analytics, and now, AI, can transform businesses. As a Chief Data and Analytics Officer with AI responsibility for two Fortune 150 companies, as an author of groundbreaking books on competing with analytics and AI in business, and as a participant and advisor on data, analytics, and AI leadership to Fortune 1000 companies, we regularly counsel leading organizations on how they must structure their executive leadership to achieve the maximum business benefit possible from these tools. So, based on our collective first-hand experience, our research and survey data, and our advisory roles with these organizations, we can state with confidence that it almost always makes the most sense to have a single leader responsible for data, analytics, and AI. While many organizations currently have several C-level tech executives, we believe that a proliferation of roles is unnecessary and ultimately unproductive. Our view is that a combined role—what we call the CDAIO (Chief Data, Analytics, and AI Officer)—will best prepare organizations as they plan for AI going forward. Here is how the CDAIO role will succeed. CDAIOs Must be Evangelists and Realists Before the 2008–09 financial crisis, data and analytics were widely seen as back-office functions, often relegated to the sidelines of corporate decision making. The crisis was a wakeup call to the absolute need for reliable data, the lack of which was seen by many as a precipitating factor of the financial crisis. In its wake, data and analytics became a C-suite function. Initially formed as a defensive function focused on risk and compliance, the Chief Data Officer (CDO) has evolved in the years since its establishment, as a growing number of firms repositioned these roles as Chief Data and Analytics Officers (CDAOs). Organizations that expanded the CDAO mandate saw an opportunity to move beyond traditional risk and compliance safeguards to focus on offense-related activities intending to use data and analytics as a tool for business growth. Once again, the role seems to be undergoing rapid change, according to forthcoming data of an annual survey that one of us (Bean) has conducted since 2012. With the rapid proliferation of AI, 53% of companies report having appointed a Chief AI Officer (or equivalent), believe that one is needed, or are expanding the CDO/CDAO mandate to include AI. AI is also leading to a greater focus and investment in data, according to 93% of respondents. These periods of evolution can be confusing to both CDAIOs and their broader organizations. Responsibilities, reporting relationships, priorities, and demands can change rapidly—as can the skills needed to do the job right. In this particular case, the massive surge in interest in AI has driven organizations to invest heavily in piloting various AI concepts. (Perhaps too frequently.) These AI initiatives have grown rapidly—and often without coordination—and leaders have been asked to orchestrate AI strategy, training data, governance, and execution across the enterprise. To address the challenges of this particular era, we believe that companies should think of the CDAIO as both evangelist and realist—a visionary storyteller who inspires the organization, a disciplined operator who focuses on projects that create value for the company while terminating those that do not deliver a return, and a strategist who deeply understands the AI technology landscape. At the core of these efforts—and essential to success in a CDAIO position—is ensuring that investments in AI and data deliver measurable business value. This has been a common stumbling block of this kind of role. The failure of data initiatives to create commensurate business value has likely contributed to the short tenures of data leaders (to say nothing of doubts about the future of the role entirely). CDAIOs need to focus on business value from day one. To Make Sure AI and Data Investments Pay Off, CDAIOs Need a Clear Mandate In most mid-to-large enterprises, data and AI touch revenue, cost, product differentiation, and risk. If trends continue, the coming decade will see systematic embedding of AI into products, processes, and customer interactions. The role of the CDAIO is to act as orchestrator of enterprise value while managing emerging risks. A single leader with a clear business mandate and close relationships with key stakeholders is essential to lead this transformation. Based on successful AI transformations we’ve observed, organizations today must entrust their CDAIOs with a mandate that includes the following: · Owning the AI strategy. To bring about any AI-enabled transformation, a single organizational leader must define the company’s “AI thesis”—how AI creates value—along with the corresponding roadmap and ROI hypothesis. The strategy needs to be sold to and endorsed by the senior executive team and the board. · Preparing for a new class of risks. AI introduces safety, privacy, IP, and regulatory risks that require unified governance beyond traditional policies. CDAIOs should normally partner with Chief Compliance or Legal Officers to manage this mandate. · Developing the AI technology stack for the company. Fragmentation and inconsistent management of tools and technology can add expense and reduce the likelihood of successful use case development. CDAIOs need the power to follow through on their vision for the adoption and development of tools and technologies that are right for the organization, providing secure “AI platforms as products” that teams can use with minimal friction. · Ensuring the company’s data is ready for AI. This is particularly critical for generative AI, which primarily uses unstructured data such as text and images. Most companies have focused only on structured numerical data in the recent past. The data quality approaches for unstructured data are both critical to success with generative AI and quite different from those for structured data. · Creating an AI-ready culture. Companies with the best AI tech might not be the long-term winners; the race will be won by those with a culture of AI adoption and effective use that maximizes value creation. CDAIOs should in most cases partner with CHROs to accomplish this objective. · Developing internal talent and external partner ecosystems. It’s essential to develop a strong talent pipeline by recruiting externally as well as upskilling internal talent. This requires building strategic alliances with technology partners and academic institutions to accelerate innovation and implementation. Generating significant ROI for the company. At the end of the day, CDAIOs need to drive measurable business outcomes—such as revenue growth, operational efficiency, and innovation velocity—by prioritizing AI initiatives tied to clear financial and strategic KPIs. They serve as the bridge between experimentation and enterprise-scale value creation. Positioning CDAIOs for Organizational Success As important as what CDAIOs are being empowered to do, is how they’re positioned in an organization to do it. Companies are adopting different models for where the CDAIO reports within the organization. While some CDAIOs report into the IT organization, others report directly to the CEO or to business area leaders. At its core, the primary role of the CDAIO is to drive business value through data, analytics, and AI, owning responsibility for business outcomes such as revenue lift and cost reduction. While AI technology enablement is a key part of the role, it is only one component of CDAIO’s broader mandate of value creation. Given the emphasis on business value creation, we believe that in most cases CDAIOs should be positioned closer to business functions than to technology operations. Early evidence suggests that only a small fraction of organizations report positive P&L impact from gen AI, a fact that underscores the need for business-first AI leadership. While we have seen successful examples of CDAIOs reporting into a technology function, this is only when the leader of that function (typically a “supertech” Chief Information Officer) is focused on technology-enabled business transformation. Today, we are witnessing a sustained trend of AI and data leadership roles reporting into business leaders. According to forthcoming survey data from this year, 42% of leading organizations report that their AI and data leadership reports to business or transformation leadership, with 33% reporting to the company’s president or Chief Operating Officer. Data, analytics, and AI are no longer back-office functions. Leading organizations like JPMorgan have made the CDAIO function part of the company’s 14-member operating committee. We see this as a direction for other organizations to follow. Whatever the reporting relationship for CDAIOs, their bosses often don’t fully understand this relatively new role and what to expect of it. To ensure success of the CDAIO role, executives to whom a CDAIO reports should maintain a checklist of the organization’s AI ambitions and CDAIO mandate. Key questions include: Do I have a single accountable leader for AI value, technology, data, risk and talent? Are AI and data roadmaps funded sufficiently against business outcomes? Are our AI risk and ethics guardrails strong enough move ahead quickly? Are we measuring AI KPIs quarterly at minimum and pivoting as needed? Are we creating measurable and sustainable value and competitive advantage with AI? The Future of AI and Data Leadership Is Here Surveys about the early CDO role reveal a consistent challenge—expectations were often unclear, and ROI was hard to demonstrate with a mission focused solely on foundational data investments. Data and AI are complementary resources. AI provides a powerful channel to show the value of data investments, but success with AI requires strong data foundations—structured data for analytical AI, and unstructured data for generative AI. Attaching data programs to AI initiatives allows demonstration of value for both, and structurally this favors a CDAIO role. The data charter (governance, platform, quality, architecture, privacy) becomes a data and platforms component within the CDAIO’s remit. Benefits include fewer hand-offs, faster decision cycles and clearer accountability. To turn AI from experiment to enterprise muscle, organizations must establish a CDAIO role with business, cultural, and technology transformation mandates. We believe strongly that the CDAIO will not be a transitional role. CEOs and other senior executives must ensure that CDAIOs are positioned for success, with resources and organizational design that supports the business, cultural, and technology mandate of the CDAIO. The demand and need for strong AI and data leadership will be essential if firms expect to compete successfully in an AI future which is arriving sooner than anyone anticipated.  Source of the article

From interactive diagrams to A.I. assistants, virtual tools are beginning to supplant physical dissections in some classrooms A human chest as large as a room fills your entire field of view. With a few commands, you shrink it down until it’s a mere speck. Then, you return it to life-size and lay it prone, where you proceed to strip off the skin and layers of viscera and muscle. Helpful text hovers in the air, explaining what you see, projected across your field of vision by a headset. This futuristic experience is becoming more commonplace in medical schools across the country, as instructors adopt virtual reality tools and other digital technologies to teach human anatomy. On dissection tables in some classrooms, where students might have once gathered around human cadavers, digitized reconstructions of the human body appear on screens, allowing students to parse the layers of bones and tendons, watch muscles contract, and navigate to specific anatomical features. Sandra Brown, a professor of occupational therapy at Jacksonville University in Florida, teaches her introductory anatomy class with exclusively digital cadavers. “In a way, the dissection is brought to life,” she says. “It’s a very visual way for [students] to learn. And they love it.” The dissection of real human cadavers has long been a cornerstone of medical education. Dissection reveals not only the form of the organs, but how the structures of the human body work together as a whole system. The best way to understand the human body, researchers have argued, is to get up close and personal with one. But human dissection has also been controversial for hundreds of years, with a history burdened by grave robbers and unscrupulous physicians. Now, with interactive diagrams, artificial intelligence assistants and virtual reality experiences, new technology might provide an effective alternative for students—no bodies necessary. Still, the shift toward these tools raises questions around what might be lost when real bodies leave the classroom—and whether dissecting a human body carries lessons that no digital substitute can teach. “Is it helpful to be exposed to death, and is there something beyond just the functional learning of dissecting a cadaver?” says Ezra Feder, a second-year medical student at the Icahn School of Medicine at Mount Sinai in New York. “I don’t really have a great answer for that.” “A new dimension of interaction” Among the most popular new additions to anatomy classrooms are digital cadaver “tables.” These giant, iPad-like screens can be wheeled into the classroom or the lab. Anatomage, a California-based company that produces one such table, has seen its product adopted by more than 4,000 health care and education institutions. The company uses real human cadavers that have been frozen and imaged in thousands of thin sheets, then reconstructs them digitally, so students can repeatedly practice differentiating layers and systems of the body. Digital cadavers are not new, but they’re getting better, more realistic and more interactive. They’re so good that some schools have phased out real human cadavers entirely. Brown, who uses the Anatomage table, says digital dissection meets the educational styles preferred by her students. “They’ve had smartphones in their hands since they were born, practically. So, the fact that we have this massive virtual technology that they can use, and they can actually start to incorporate all the skills they have into learning—it was just a no-brainer for me,” she says. “It’s really fun.” Brown’s students can rotate, move and manipulate the digital cadavers in ways that would be impossible with a real body. “They literally have the brain upside down, and they’re looking at it from underneath. You can’t really do a lot of that when you have a cadaver in front of you, because they’re so fragile,” she says. “It’s an errorless way for [students] to explore, because if they make a mistake, or they can’t find something, they can reset it, and they can undo it.” Other companies, like Surglasses, which developed the Asclepius AI Table, are taking the digital cadaver model one step further. This table features A.I. assistants with human avatars that can listen and respond to voice commands from students and educators. The assistants can pull up relevant images on the table and quiz students on what they’ve learned. Recent research has shown that A.I. assistants can effectively support student learning and that those with avatars are particularly promising. “Students really respond well to technology that’s accessible to them,” says Saeed Juggan, a graduate teaching assistant at Yale Medical School, which has its own suite of digital anatomy tools, including a 3D model of a body that students can access from their own devices. Still, Juggan is a bit wary of A.I. tools because of potential limitations with the data they’re trained on. “Suppose students ask a question that’s not answered by those resources. What do you do in that case? And what do you tell the bot to do in that case?” he says. With virtual and augmented reality (VR/AR) anatomy programs, human dissection has become even more futuristic. Companies like Toltech have created VR headsets that transport students into an immersive digital cadaver lab, where they manipulate a detailed, annotated body standing in a gray void. While learning remotely during the Covid-19 pandemic, students at Case Western Reserve University donned bug-like visors to interact with holographic bodies that appeared to be floating in the students’ apartments. Still, VR comes with complications. Some students experience motion sickness from the headsets, explains Kristen Ramirez, a research instructor and content director for anatomy lab at New York University’s Grossman School of Medicine. Her approach, and that of her team at NYU, is to tailor the technology to fit the type and content of instruction. Ramirez and a colleague have created an in-house VR program that allows students to stand inside a human heart. Students can see “everything that the red blood cells would have seen if they had eyes and cognition,” she says. For certain parts of the body, an immersive experience is the best way to understand them, Ramirez adds. The pterygopalatine fossa, for example, is a small space deep inside the face, roughly between the cheek and nose—and the only way to see it, until now, has been by sawing through a donor’s skull. Even then, the fragile structures are inevitably damaged. With VR, students can view that cavity as though they are standing inside it—the “Grand Central Station of the head and neck,” as Ramirez calls it—and access “literally a new dimension of interaction.” The body on the table Even as digital tools land in more medical school classrooms, some say that learning from an actual body is irreplaceable. William Stewart, an associate professor of surgery at Yale University who instructs gross anatomy, sees the embodied experience of dissection as vital. “There’s a view of learning called ‘gestalt,’ which is that learning is the sum of all of the senses as the experience occurs,” he explains. “There’s seeing, there’s touching, there’s camaraderie around the table. There’s—I know this sounds silly, but it’s true—there’s the smell,” Stewart says. “All of those contribute, in one way or other, to the knowledge, and the more and more of those senses you take away, in my view, the less and less you learn.” When it comes to preparing for surgery or getting tactile experience with a body, surveys suggest students generally favor cadaver dissection, with some citing better retention of concepts. Working solely with digitized, color-coded models that can respond to voice commands does students a disservice, Stewart argues. “It’s not seeing it, it’s finding it that makes the knowledge.” The donation of one’s body to scientific learning and research is not taken lightly. It’s common practice for medical students to participate in a memorial service to honor the people who they will dissect as part of their studies. Jai Khurana, a first-year medical student at the Harvard-MIT Health Sciences Technology Program currently taking introductory anatomy, describes a respect and care for the human body that he and his fellow students learn through human dissection. “We regularly stay many hours past our anatomy lab if we don’t think we’re going to finish,” he says. “You still want to finish what you’re doing, do it in a respectful way and learn everything that you can learn.” Still, ethical violations have long plagued human dissection. In the 18th and 19th centuries, medical students dissected corpses stolen from graves and even those that had been murdered and sold by unscrupulous profiteers. At the time, dissection was implemented as an extra punishment for executed criminals in Britain to deprive them of a Christian burial; the boon to medical research was a bonus. Today, some countries around the world and many U.S. states still permit the dissection of “unclaimed remains,” or the bodies of those who die without family to properly bury them, raising concerns about consent. A recent investigation revealed that unclaimed corpses sent to the University of Southern California’s anatomy program were sold to the U.S. Navy, where they were used to train military operatives in the Israel Defense Forces. And despite the fact that most medical school cadavers in the U.S. are willingly donated, the donors and families are sometimes underinformed about what may happen to their remains. No federal agency monitors what happens to bodies donated for research and education. In an extreme case from this year, the former manager of the Harvard morgue pleaded guilty to stealing donated human remains and selling them to retailers for profit. Digital cadavers, VR/AR and A.I.-enhanced anatomy technology could offer a way to skirt these issues by reducing the number of human bodies needed for education—and the cutting-edge tech might actually be less costly than human cadavers for some medical programs. Whatever way you swing it, bodies are expensive, even if they are donated. Supporting a cadaver lab requires administrative staff to coordinate body donations, a wet lab space equipped for dissections and infrastructure for disposing of human remains. Because of this, students typically work in small groups to use fewer bodies. Each human cadaver can be used only once for each procedure, but digital ones can be reset repeatedly. For Brown, who teaches with the Anatomage table, the ideal lab would be a mix of synthetic, digital and real human dissection, where she could supplement a largely digital cadaver-based education with different tools to demonstrate various elements of anatomy. But given the financial constraints at Jacksonville University, Brown does what she can with the Anatomage table, having her students rotate the body’s shoulders, color code structures and create videos of their work to reference later. Her occupational therapy students are not preparing to be surgeons, so they would not have to practice cutting into flesh, she adds. Learning from a human cadaver has long been considered a rite of passage for medical students, who typically must dissect a body during their first-year anatomy class. But the emotional weight of human dissection can sometimes hinder, not enhance, the experience. “Cadavers can be scary, like a dead body laying in front of you that you have to look at,” says Brown. “And I think that [digital dissection] is just a safe way for [students] to explore.” She explains that some students enter the program expecting cadaver dissection and feel more comfortable when they encounter the digital models instead. Perhaps there’s value to sitting with that discomfort. Ramirez says that students who were initially apprehensive to human dissection might never overcome their squeamishness when offered a virtual alternative. “Because they are getting such small moments of interaction with the cadavers, I definitely will still see students even a couple weeks in, you know, disappointed, if you will, that they’re at a cadaver station, hesitant about going and interacting with it,” she says. For Feder, the student at Mount Sinai, dissecting a real human body elicited mixed emotions. In the lab, some students seemed to become desensitized over time to the cadaver’s humanity and treated it inappropriately, he says. “For some people, it became so ordinary and routine that maybe they lost some respect for the body,” Feder adds. “Maybe these are coping mechanisms.” Regarding respect for the dead, he says, “I think I’d feel a lot more comfortable learning from a technology-based cadaver than a real human bone-and-flesh cadaver.” Educationally, the physical cadaver dissection “was really invaluable,” Feder adds, and “a little bit hard to replace.” But he notes that the value of being exposed to death might vary between students, depending on what exactly they’re training for. “Overall, most doctors are in the business of keeping people alive.” The future of learning from cadavers While anatomy classes are increasingly using digitized bodies, cadaver dissection writ large is not likely to disappear anytime soon. It’s still a common way for surgeons to gain tactile experience with manipulating human flesh. Juggan, the Yale graduate student, explains that a neurosurgeon recently practiced a hemispherectomy on cadavers at the university before operating on a living patient. The procedure, which entails surgically separating different parts of the brain, is difficult, with a potential for catastrophic failure. Practicing this surgery on a cadaver is “not necessarily looking at the tissue,” Juggan says. “It’s getting the muscle memory. It’s getting the tactile feel for … this anatomical structure.” It goes without saying: No living patient wants to be the beta test for brain surgery. But not all cadavers are used to prep for such dramatic, high-stakes operations. As Mary Roach observes in her book Stiff: The Curious Lives of Human Cadavers, even plastic surgeons practice nose jobs on disembodied heads from donors. “Perhaps there ought to be a box for people to check or not check on their body donor form: Okay to use me for cosmetic purposes,” she writes in the book. Though the tactile training of surgeons remains important, surgery itself is getting more technologically advanced. With more robot-assisted surgeries, it’s not hard to imagine that technology and anatomy teaching will become more deeply integrated. Take, for instance, laparoscopic surgery, meant to look inside the pelvis or stomach by inserting a tiny camera into the abdomen. The surgery feels almost like science fiction: The surgeon makes minute adjustments from a distance, while the patient’s organs appear on a glowing screen. “If you’re doing laparoscopic surgery, you’re putting three tiny holes in the abdomen, and you’re playing a video game,” Ramirez says. There’s a conceivable future where the majority of medical students do not dissect an actual human body. The problem of tactile experience might also soon be solved by innovation, as synthetic cadavers—made of thermoplastic and organosilicate—mimic the physicality of the human body without limitations of ethics or decomposition. The anatomy classroom may soon be filled with digital dead people and synthetic approximations of flesh, rather than a decaying memento mori. Death’s banishment is, after all, in service of keeping more people alive longer. Source of the article